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Geoforensic analyses are used to identify the location history of objects or people of interest. An effective method for location history identification is to use joint probability or suitability of trace materials. Species distribution models have been used to derive joint suitability distributions using suitable biotic trace evidence such as pollen. One of the key objectives for such analyses is to effectively reduce potential search space and search effort for investigators. This research presents a novel framework for modeling the habitat suitability of pollen identified at the plant species-level to generate joint suitability maps. We provide major limitations and challenges faced by current geolocation analyses based on species distribution models, including opportunities to improve the joint suitability analyses for search space reduction. A conditional probability approach for geolocation identification is also demonstrated for possible future applications in real-world forensic cases.

Our aim is to make a step toward clarification of foundations for the notion of entanglement (both physical and mathematical) by representing it in the conditional probability framework. In Schrodinger's words, this is entanglement of knowledge which can be extracted via conditional measurements. In particular, quantum probabilities are interpreted as conditional ones (as, e.g., by Ballentine). We restrict considerations to perfect conditional correlations (PCC) induced by measurements ('EPR entanglement'). Such entanglement is coupled to the pairs of observables with the projection type state update as the back action of measurement. In this way, we determine a special class of entangled states. One of our aims is to decouple the notion of entanglement from the compound systems. The rigid association of entanglement with the state of a few body systems stimulated its linking with quantum nonlocality ('spooky action at a distance'). However, already by Schrodinger entanglement was presented as knotting of knowledge (about statistics) for one observable A with knowledge about another observable B.

Our aim is to make a step toward clarification of foundations for the notion of entanglement (both physical and mathematical) by representing it in the conditional probability framework. In Schrodinger's words, this is entanglement of knowledge which can be extracted via conditional measurements. In particular, quantum probabilities are interpreted as conditional ones (as, e.g., by Ballentine). We restrict considerations to perfect conditional correlations (PCC) induced by measurements ('EPR entanglement'). Such entanglement is coupled to the pairs of observables with the projection type state update as the back action of measurement. In this way, we determine a special class of entangled states. One of our aims is to decouple the notion of entanglement from the compound systems. The rigid association of entanglement with the state of a few body systems stimulated its linking with quantum nonlocality ('spooky action at a distance'). However, already by Schrodinger entanglement was presented as knotting of knowledge (about statistics) for one observable A with knowledge about another observable B.

Our aim is to make a step toward clarification of foundations for the notion of entanglement (both physical and mathematical) by representing it in the conditional probability framework. In Schrodinger's words, this is entanglement of knowledge which can be extracted via conditional measurements. In particular, quantum probabilities are interpreted as conditional ones (as, e.g., by Ballentine). We restrict considerations to perfect conditional correlations (PCC) induced by measurements ('EPR entanglement'). Such entanglement is coupled to the pairs of observables with the projection type state update as the back action of measurement. In this way, we determine a special class of entangled states. One of our aims is to decouple the notion of entanglement from the compound systems. The rigid association of entanglement with the state of a few body systems stimulated its linking with quantum nonlocality ('spooky action at a distance'). However, already by Schrodinger entanglement was presented as knotting of knowledge (about statistics) for one observable A with knowledge about another observable B.

Our aim is to make a step toward clarification of foundations for the notion of entanglement (both physical and mathematical) by representing it in the conditional probability framework. In Schrodinger's words, this is entanglement of knowledge which can be extracted via conditional measurements. In particular, quantum probabilities are interpreted as conditional ones (as, e.g., by Ballentine). We restrict considerations to perfect conditional correlations (PCC) induced by measurements ('EPR entanglement'). Such entanglement is coupled to the pairs of observables with the projection type state update as the back action of measurement. In this way, we determine a special class of entangled states. One of our aims is to decouple the notion of entanglement from the compound systems. The rigid association of entanglement with the state of a few body systems stimulated its linking with quantum nonlocality ('spooky action at a distance'). However, already by Schrodinger entanglement was presented as knotting of knowledge (about statistics) for one observable A with knowledge about another observable B.

This thesis is written on the subject of Mathematical Didactics. The intention of this thesis is to examine how conditional probability and independence should be taught such that students acquire solid knowledge about the subject that is conceptualized in a non-misleading way. This thesis contains a presentation of the place of conditional probability and independence in curricula and textbooks today and within the past 15 years. This is followed by a review of preliminary theory on how misconception can be avoided in conditional probability and independence. This review is used to design a teaching course of five lessons of 55 minutes, that aims to prevent students from forming misconceptions and to increase students' linguistic and formalistic abilities. This didactic design is made within the framework of the Theory of Didactic Situations, which is also explained. The developed design has been tested on a second-year class in a Danish upper secondary school. The thesis contains an a priori analysis, which was made before this design was tested, and an a posteriori analysis of the collected data and observations. Based on this, it can be concluded that it takes more than five lessons to deconceptualize the students' misconceptions. It can also be concluded that it is possible to increase the students' linguistic abilities, but it requires changes in the teaching design and a new study to be able to conclude that the students' formalistic abilities can be improved.

This thesis is written on the subject of Mathematical Didactics. The intention of this thesis is to examine how conditional probability and independence should be taught such that students acquire solid knowledge about the subject that is conceptualized in a non-misleading way. This thesis contains a presentation of the place of conditional probability and independence in curricula and textbooks today and within the past 15 years. This is followed by a review of preliminary theory on how misconception can be avoided in conditional probability and independence. This review is used to design a teaching course of five lessons of 55 minutes, that aims to prevent students from forming misconceptions and to increase students' linguistic and formalistic abilities. This didactic design is made within the framework of the Theory of Didactic Situations, which is also explained. The developed design has been tested on a second-year class in a Danish upper secondary school. The thesis contains an a priori analysis, which was made before this design was tested, and an a posteriori analysis of the collected data and observations. Based on this, it can be concluded that it takes more than five lessons to deconceptualize the students' misconceptions. It can also be concluded that it is possible to increase the students' linguistic abilities, but it requires changes in the teaching design and a new study to be able to conclude that the students' formalistic abilities can be improved.

Dairy farms are systems with multiple dependent variables whose practices influence their economic and environmental performances. Decisions made and actions taken to improve environmental performances of dairy farms carry the risk of decreasing farm profitability. Correlations among multiple variables must therefore be considered to reliably assess risks of improving environmental performances of farms. We applied copula models to a dataset of conventional dairy farms surveyed in France to decscribe relationships among their characteristics, such as forage dry matter (DM) production, milk production, and greenhouse gas (GHG) emissions. By modeling relationships among farm characteristics, copula models can identify the characteristics’ joint distributions, unlike other statistical methods. For dairy farms, copula models are useful for estimating probabilities of reaching a milk production goal or not exceeding a regulatory emission limit as a function of forage production. For instance, when a farm produced at least 4,500 kg DM/livestock unit/year of maize silage, the probability of producing at least 7,000 l milk/cow/year was 75%, while the probability of emitting less than 7,000 kg CO2 eq./farm/year (a value close to the mean of 6669 kg CO2 eq./farm/year for all of the farms) was 48%. When the same amount of grass from pasture was produced, these probabilities changed to 48% and 78%, respectively (i.e., decreased probability of reaching a production goal, but increased probability of not exceeding an emission threshold). Farmers must make trade-offs, since increased milk production goals are likely to increase GHG emissions per farm and/or reduce GHG emission intensities per l of milk, but are less likely to be reached for a given amount of forage DM. By providing information about relationships among farm characteristics that other statistical approaches cannot, copula models are useful for investigating these trade-offs.

During a rainy season, dry and wet spells tend to persist and can be represented using a Markov process. Knowing the succession of dry and wet periods is necessary to plant crops and carry out agricultural operations. This study aimed to analyze the probability of dry/wet spell rainfall using the Markov chain model in the Dharmapuri district of Tamil Nadu, India. In estimating the chance of dry and wet spells, the model used rainfall of below 20 mm in a week as a dry calendar week and rainfall of 20 mm or more as a wet calendar week from the years 1980 to 2019. From the 1st through the 32nd Standard Meteorological Week (SMW), a continuous dry week probability was 75-100%. The likelihood of a dry week trailed by another dry week was more up to the 32nd standard week, while the chance of a dry week followed by a wet week was more up to the 31st standard week, ranging from 75 to 100%. During the 37th to 45th weeks, the conditional likelihood of a rainy week followed by another rainy week ranged from 43.8 to 68%. According to a review of consecutive dry and wet spells, two consecutive dry weeks had a 55 to 97.5% chance of occurring within the first 32 weeks of the year. In the first 32nd week of the year, the chance of three successive dry weeks ranged from 32.6 to 92.6%. Consecutive dry weeks suggest the need for additional irrigation and proper moisture management practices. In contrast, consecutive wet calendar weeks indicate an abundance of extra water available for rainwater collection and the necessity for proper soil erosion control measures.

Our aim is to make a step toward clarification of foundations for the notion of entanglement (both physical and mathematical) by representing it in the conditional probability framework. In Schrodinger's words, this is entanglement of knowledge which can be extracted via conditional measurements. In particular, quantum probabilities are interpreted as conditional ones (as, e.g., by Ballentine). We restrict considerations to perfect conditional correlations (PCC) induced by measurements ('EPR entanglement'). Such entanglement is coupled to the pairs of observables with the projection type state update as the back action of measurement. In this way, we determine a special class of entangled states. One of our aims is to decouple the notion of entanglement from the compound systems. The rigid association of entanglement with the state of a few body systems stimulated its linking with quantum nonlocality ('spooky action at a distance'). However, already by Schrodinger entanglement was presented as knotting of knowledge (about statistics) for one observable A with knowledge about another observable B.

The objective of this research is to model the behavior of oil returns. The volatility of oil returns is described through a TGARCH process. Conditional probability jumps are incorporated through uniform, double exponential and normal jump intensity distributions. We found that the volatility of oil returns follows the stylized facts of leptokurtosis, leverage effect and volatility clustering. The abnormal information that causes the jumps, can cause another type of unexpected changes in the following period and the intensity of the jumps has a negative effect on the probability of jumps in the next period. The dynamic model proposed can be extended to other markets and to multivariate time series modeling considering the dependence among the markets’ returns. The main contribution of this work is the estimation of the conditional probability of jumps depending on the previous behavior leading to a better description of the stochastic dynamics of crude oil prices. This will be useful for making better decisions regarding oil as an underlying asset in derivatives or in the formulation of better public policies. JEL Classification: C10, C22, G13, Q4., Probabilidad condicional de saltos en precios del petróleo Esta investigación modela el comportamiento de rendimientos del petróleo. La volatilidad de estos rendimientos se describe con un proceso TGARCH. La probabilidad de saltos condicionales se incorpora mediante distribuciones de intensidad de salto uniformes, doble exponencial y normal. Descubrimos que la volatilidad de estos rendimientos sigue los hechos estilizados de leptocurtosis, efecto de apalancamiento y agrupamiento de volatilidad. La información anormal que causa los saltos provoca otro tipo de cambios inesperados en el siguiente período y la intensidad de los saltos tiene un efecto negativo en la probabilidad de saltos en el siguiente período. El modelo dinámico propuesto puede extenderse a otros mercados y a modelos de series de tiempo multivariadas considerando la dependencia entre los rendimientos de los mercados. La principal contribución del trabajo es la estimación de la probabilidad condicional de saltos en función del comportamiento anterior que conduce a una mejor descripción de la dinámica estocástica de los precios del petróleo. Esto será útil para tomar mejores decisiones con respecto al petróleo como activo subyacente en derivados o en la formulación de mejores políticas públicas.

An ecosystem approach to fisheries management is being developed around the world in an attempt to consider components of marine ecosystems other than just the exploited stocks. While numerous scientific studies on the consequences of fishing on marine ecosystems exist, most of their findings are too uncertain and fail to quantify the magnitude of the effects they describe to be used reliably in an environmental management and marine policy-making realm. To circumvent such a knowledge gap, we built and fitted a Bayesian network (BN), informed by a review of past studies, which integrates the links and direction of effects between socio-ecosystem components. These effects are represented as conditional probabilities, so that missing magnitudes of some ecosystem effects emerge from the numerous past cases that were reviewed. The Bayesian network is informed by a collection of cases extracted from 246 published scientific studies investigating relationships in marine ecosystems. We find that marine ecosystems are likely to be on a declining course under conjugated pressures of both fishing and changes to environmental conditions, e.g. due to ongoing climate change. By querying the fitted BN to obtain posterior probabilities under different scenarios, we showed that increasing fisheries regulation and environmental governance could partly mitigate these effects and decrease the risk of biodiversity loss, decreased profit and social inequity; the three pillars of the EU Common Fishery Policy (CFP). We discuss these findings with regard to particular fisheries across EU Waters, specifically: the North Sea, the Baltic Sea, the North Western and the South Western Waters. Furthermore, we discuss how fishing impacts interact with other ecosystem effects and pressures, caused by, or causing possible far-reaching consequences in marine ecosystem dynamics. The Bayesian network has some limitations regarding the ability to handle feedback loops that can occur in natural marine ec

Aim: To compare fit statistics for the Rasch model based on estimates of unconditional or conditional response probabilities. Background: Using person estimates to calculate fit statistics can lead to problems because the person estimates are biased. Conditional response probabilities given the total person score could be used instead. Methods: Data sets are simulated which fit the Rasch model. Type I error rates are calculated and the distributions of the fit statistics are compared with the assumed normal or chi-square distribution. Parametric bootstrap is used to further study the distributions of the fit statistics. Results: Type I error rates for unconditional chi-square statistics are larger than expected even for moderate sample sizes. The conditional chi-square statistics maintain the significance level. Unconditional outfit and infit statistics have asymmetric distributions with means slighly below 1. Conditional outfit and infit statistics have reduced Type I error rates. Conclusions: Conditional residuals should be used. If only unconditional residuals are available parametric bootstrapping is recommended to calculate valid p-values. Bootstrapping is also necessary for conditional outfit statistics. For conditional infit statistics the adjusted rule-of-thumb critical values look useful.

Aim: To compare fit statistics for the Rasch model based on estimates of unconditional or conditional response probabilities. Background: Using person estimates to calculate fit statistics can lead to problems because the person estimates are biased. Conditional response probabilities given the total person score could be used instead. Methods: Data sets are simulated which fit the Rasch model. Type I error rates are calculated and the distributions of the fit statistics are compared with the assumed normal or chi-square distribution. Parametric bootstrap is used to further study the distributions of the fit statistics. Results: Type I error rates for unconditional chi-square statistics are larger than expected even for moderate sample sizes. The conditional chi-square statistics maintain the significance level. Unconditional outfit and infit statistics have asymmetric distributions with means slighly below 1. Conditional outfit and infit statistics have reduced Type I error rates. Conclusions: Conditional residuals should be used. If only unconditional residuals are available parametric bootstrapping is recommended to calculate valid p-values. Bootstrapping is also necessary for conditional outfit statistics. For conditional infit statistics the adjusted rule-of-thumb critical values look useful.

This article shows evidence of the knowledge of seven teachers about the conditional probability student learning features. It is a qualitative study of an interpretive nature. We used the Mathematics Teacher’s Specialised Knowledge model and the Bottom-Upand Top-Down analysis perspective to characterize the knowledge evidenced in a questionnaire and an inter-view. The results show that recognizing bias, fallacies and confusions con-tributes to the identification, by the teacher, of the strengths, difficulties and interaction that students have in conditional probability. We propose new indicators of pedagogical content knowledge that allow understand-ing of cognitive configurations of students., Este artículo muestra evidencias del conocimiento de siete profesores sobre las características de aprendizaje de estudiantes en probabilidad condicional. Se trata de un estudio cualitativo de corte interpretativo. Usamos el modelo Mathematics Teacher’s Specialised Knowledge y la perspectiva de análisis Bottom-Up y Top-Down para caracterizar el conocimiento evidenciado en un cuestionario y una entrevista. Los resultados muestran que reconocer sesgo, falacias y confusiones contribuye a identificar, por el profesor, las fortalezas, dificultades e interacción que tienen los estudiantes en probabilidad condicional. Proponemos nuevos indicadores de conocimiento didáctico del contenido que permiten comprender configuraciones cognitivas de estudiantes.

This article shows evidence of the knowledge of seven teachers about the conditional probability student learning features. It is a qualitative study of an interpretive nature. We used the Mathematics Teacher’s Specialised Knowledge model and the Bottom-Upand Top-Down analysis perspective to characterize the knowledge evidenced in a questionnaire and an inter-view. The results show that recognizing bias, fallacies and confusions con-tributes to the identification, by the teacher, of the strengths, difficulties and interaction that students have in conditional probability. We propose new indicators of pedagogical content knowledge that allow understand-ing of cognitive configurations of students., Este artículo muestra evidencias del conocimiento de siete profesores sobre las características de aprendizaje de estudiantes en probabilidad condicional. Se trata de un estudio cualitativo de corte interpretativo. Usamos el modelo Mathematics Teacher’s Specialised Knowledge y la perspectiva de análisis Bottom-Up y Top-Down para caracterizar el conocimiento evidenciado en un cuestionario y una entrevista. Los resultados muestran que reconocer sesgo, falacias y confusiones contribuye a identificar, por el profesor, las fortalezas, dificultades e interacción que tienen los estudiantes en probabilidad condicional. Proponemos nuevos indicadores de conocimiento didáctico del contenido que permiten comprender configuraciones cognitivas de estudiantes.

This article shows evidence of the knowledge of seven teachers about the conditional probability student learning features. It is a qualitative study of an interpretive nature. We used the Mathematics Teacher’s Specialised Knowledge model and the Bottom-Upand Top-Down analysis perspective to characterize the knowledge evidenced in a questionnaire and an inter-view. The results show that recognizing bias, fallacies and confusions con-tributes to the identification, by the teacher, of the strengths, difficulties and interaction that students have in conditional probability. We propose new indicators of pedagogical content knowledge that allow understand-ing of cognitive configurations of students., Este artículo muestra evidencias del conocimiento de siete profesores sobre las características de aprendizaje de estudiantes en probabilidad condicional. Se trata de un estudio cualitativo de corte interpretativo. Usamos el modelo Mathematics Teacher’s Specialised Knowledge y la perspectiva de análisis Bottom-Up y Top-Down para caracterizar el conocimiento evidenciado en un cuestionario y una entrevista. Los resultados muestran que reconocer sesgo, falacias y confusiones contribuye a identificar, por el profesor, las fortalezas, dificultades e interacción que tienen los estudiantes en probabilidad condicional. Proponemos nuevos indicadores de conocimiento didáctico del contenido que permiten comprender configuraciones cognitivas de estudiantes.

Aim: To compare fit statistics for the Rasch model based on estimates of unconditional or conditional response probabilities. Background: Using person estimates to calculate fit statistics can lead to problems because the person estimates are biased. Conditional response probabilities given the total person score could be used instead. Methods: Data sets are simulated which fit the Rasch model. Type I error rates are calculated and the distributions of the fit statistics are compared with the assumed normal or chi-square distribution. Parametric bootstrap is used to further study the distributions of the fit statistics. Results: Type I error rates for unconditional chi-square statistics are larger than expected even for moderate sample sizes. The conditional chi-square statistics maintain the significance level. Unconditional outfit and infit statistics have asymmetric distributions with means slighly below 1. Conditional outfit and infit statistics have reduced Type I error rates. Conclusions: Conditional residuals should be used. If only unconditional residuals are available parametric bootstrapping is recommended to calculate valid p-values. Bootstrapping is also necessary for conditional outfit statistics. For conditional infit statistics the adjusted rule-of-thumb critical values look useful.

This paper presents an investigation on the structure of conditional events and on the probability measures which arise naturally in that context. In particular we introduce a construction which defines a (finite) Boolean algebra of conditionals from any (finite) Boolean algebra of events. By doing so we distinguish the properties of conditional events which depend on probability and those which are intrinsic to the logico-algebraic structure of conditionals. Our main result provides a way to regard standard two-place conditional probabilities as one-place probability functions on conditional events. We also consider a logical counterpart of our Boolean algebras of conditionals with links to preferential consequence relations for non-monotonic reasoning. The overall framework of this paper provides a novel perspective on the rich interplay between logic and probability in the representation of conditional knowledge.

Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk factors of the earth-rock dam breach are complex, which vary with time during the operation period. Static risk analysis, limited to a specific period of time, cannot meet the needs of comprehensive assessment and early warning. By introducing time factors, a dynamic Bayesian network model was established to study the dynamic characteristics of dam-breach probability. Combined with the calculation of the conditional probability of nodes based on the Leaky Noisy-Or gate extended model, the reasoning results of Bayesian networks were modified by updating the data of different time nodes. Taking an earth-rock dam as an example, the results show that it has less possibility to breach and keep stable along the time axis. Moreover, the factors with vulnerability and instability were found effective, which could provide guidance for dam risk management., Safety and Security Science

When dealing with the optic disc and cup in the optical nerve head images, their joint segmentation confronts two critical problems. One is that the spatial layout of the vessels in the optic nerve head images is variant. The other is that the landmarks for the optic cup boundaries are spatially sparse and at small spatial scale. To solve these two problems, we propose a spatial-aware joint segmentation method by explicitly considering the spatial locations of the pixels and learning the multi-scale spatially dense features. We formulate the joint segmentation task from a probabilistic perspective, and derive a spatial-aware maximum conditional probability framework and the corresponding error function. Accordingly, we provide an end-to-end solution by designing a spatial-aware neural network. It consists of three modules: the atrous CNN module to extract the spatially dense features, the pyramid filtering module to produce the spatial-aware multi-scale features, and the spatial-aware segmentation module to predict the labels of pixels. We validate the state-of-the-art performances of our spatial-aware segmentation method on two public datasets, i.e., ORIGA and DRISHTI. Based on the segmentation masks, we quantify the cup-to-disk values and apply them to the glaucoma screening. High correlation between the cup-to-disk values and the risks of the glaucoma is validated on the dataset ORIGA.

Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk factors of the earth-rock dam breach are complex, which vary with time during the operation period. Static risk analysis, limited to a specific period of time, cannot meet the needs of comprehensive assessment and early warning. By introducing time factors, a dynamic Bayesian network model was established to study the dynamic characteristics of dam-breach probability. Combined with the calculation of the conditional probability of nodes based on the Leaky Noisy-Or gate extended model, the reasoning results of Bayesian networks were modified by updating the data of different time nodes. Taking an earth-rock dam as an example, the results show that it has less possibility to breach and keep stable along the time axis. Moreover, the factors with vulnerability and instability were found effective, which could provide guidance for dam risk management., Safety and Security Science

We start with a review on classical probability representations of quantum states and observables. We show that the correlations of the observables involved in the Bohm-Bell type experiments can be expressed as correlations of classical random variables. The main part of the paper is devoted to the conditional probability model with conditioning on the selection of the pairs of experimental settings. From the viewpoint of quantum foundations, this is a local contextual hidden-variables model. Following the recent works of Dzhafarov and collaborators, we apply our conditional probability approach to characterize (no-)signaling. Consideration of the Bohm-Bell experimental scheme in the presence of signaling is important for applications outside quantum mechanics, e.g., in psychology and social science. The main message of this paper (rooted to Ballentine) is that quantum probabilities and more generally probabilities related to the Bohm-Bell type experiments (not only in physics, but also in psychology, sociology, game theory, economics, and finances) can be classically represented as conditional probabilities.

A principal goal of volcanology is to successfully forecast the start of volcanic eruptions. This paper introduces a general forecasting method, which relies on a stream of monitoring data and a statistical description of a given threshold criterion for an eruption to start. Specifically we investigate the timing of intrusive and eruptive events at inflating volcanoes. The gradual inflation of the ground surface is a well-known phenomenon at many volcanoes and is attributable to pressurised magma accumulating within a shallow chamber. Inflation usually culminates in a rapid deflation event caused by magma escaping from the chamber to produce a shallow intrusion and, in some cases, a volcanic eruption. We show that the ground elevation during 15 inflation periods at Krafla volcano, Iceland, increased with time towards a limiting value by following a decaying exponential with characteristic timescale τ. The available data for Krafla, Kilauea and Mauna Loa volcanoes show that the duration of inflation (t ⁎) is approximately equal to τ. The distribution of t ⁎/τ values follows a log-logistic distribution in which the central 60% of the data lie between 0.99

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.

Objective: The concept of specific agreement (SA) has been proposed for dichotomous outcomes for two or more raters. We aim to extend this concept for variables with more than two ordinal or nominal categories and more than two raters. Study Design and Setting: We used two data sets: four plastic surgeons classifying photographs after breast reconstruction on a 5-point ordinal scale and six raters classifying psychiatric patients into five diagnostic categories. For m raters, all (i.e., m(m−1)/2) pairwise agreement tables were summed to calculate the observed agreement (OA), SA and conditional probabilities. The 95% confidence intervals were obtained by bootstrapping. Results: SA was calculated for each ordinal or nominal category to examine when one of the raters scored in a specific category, what is the probability that the other raters scored in that same category. And suppose one of the raters scored X1, what is the probability that the other raters scored X1 or any of the other categories (conditional probability). It appeared, for example, that among the psychiatric disorders, depression and personality disorders were often mixed up, whereas neurosis was rarely mixed up with schizophrenia. Conclusion: The concept of SA for variables with ordinal and multiple nominal categories provides relevant clinical information. The extension to conditional probabilities of alternative categories broadens the clinical application with examining which categories are most often mixed up. © 2017 Elsevier Inc.

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.

Techniques for anomaly detection in the maritime domain are developed in this thesis using an area metric that measures the degree of similarity, or distinction, between ships’ tracks using the area between ships’ tracks. A modified k-means algorithm is applied to the areas calculated between ships tracks to extract sea lanes and classify these ships’ tracks into sea lanes. Bayes’ theorem of conditional probabilities is used to calculate the probability of ship tracks being in different sea lanes. Two types of anomaly detection are examined in this thesis. Ships not sailing in standard sea lanes are detected using Bayes’ theorem, and ships following one another are detected using user-defined thresholds on the minimum area between sea tracks. The development of the metric that compares ships’ track data using area is the most significant result of this thesis., http://archive.org/details/maritimedomainaw1094560384, Navy, Pakistan, Approved for public release; distribution is unlimited.

The present research is focused in the difficulty that the students show in the approach of adequate solutions to problems that are commonly used in the field of Probability and Statistics at the College. It was found that both the various issues addressed in the problems of probability, the cells are not familiar to students, in addition to the language that is commonly used in the texts, causes conflict to propose an appropriate approach in their solutions. The conceptual framework that supports the study in relation to the link between language and mathematics are the ideas of Chomsky on the importance of clarifying semantic aspects in the problems of mathematics and those of Duval who emphasizes the representations and their implications for the study of the teaching and learning processes of mathematics. In relation to the development of the study, it has a constructivist social approach due to the contexts that address the problems with which they work. Both the students and the teachers who participated in the study were notified of the work that would be done, respecting their decision in the participation of the same. The experimental methodology was used when working with a group of 32 students who studied the subject of Probability and Statistics in the third semester of the field of Engineering in Computer Systems. First a diagnostic questionnaire was applied to know its perception in relation to the use of technology, after and during a month was worked with the group different applications and finally an evaluation questionnaire was applied. The results indicated that some resources or strategies that help in the understanding of the problems, is the use of videographic material that represents the presented situations, allowing the student to visualize various aspects of the problems and not only work in the algebraic terrain and with the exclusive use of formulas. In addition the formulation of several questions that lead the student both to the dete, Na presente investigação, aborda-se o problema da dificuldade que os alunos apresentam na abordagem de soluções adequadas aos problemas que são comumente usados na questão de Probabilidade e Estatística no nível superior. Verificou-se que ambas as várias questões abordadas nos problemas de probabilidade, que não são familiares para os alunos, além da linguagem que é usada nos textos, provocam conflitos e uma interpretação inadequada dos problemas, o que os leva a propor uma abordagem errada em suas soluções. O quadro conceitual que apóia esse estudo em relação à relação entre linguagem e matemática vem das idéias de Chomsky que enfatizam a importância de esclarecer aspectos semânticos em problemas de matemática, bem como os de Duval, que enfatiza as representações e suas implicações para o estudo dos processos de ensino e aprendizagem de matemática. Com respeito ao seu desenvolvimento, este estudo tem uma abordagem social construtivista devido aos contextos que abordam os problemas com os quais eles trabalham. Uma metodologia experimental foi utilizada ao trabalhar com um grupo de 32 alunos que estudaram o tema de Probabilidade e Estatística em seu terceiro semestre do curso de Engenharia de Sistemas Informáticos. Durante o processo de ensino e aprendizagem deste assunto, o uso de diferentes plataformas e aplicações tecnológicas foi proposto para ter uma abordagem diferente da usual. Estudantes e professores que participaram do estudo foram notificados do trabalho que seria feito, respeitando sua decisão na participação deste. Em primeiro lugar, foi aplicado um questionário para conhecer sua percepção em relação ao uso da tecnologia; Depois e durante um mês, trabalhamos com o grupo com diferentes aplicações tecnológicas; e, finalmente, foi aplicado um questionário de avaliação. Os resultados indicaram que um dos recursos ou estratégias que ajudam na compreensão dos problemas é o uso de material videográfico que representa as situações colocadas, permitindo que o alu, En la presente investigación se aborda la problemática de la dificultad que presentan los estudiantes en el planteamiento de soluciones adecuadas a problemas que comúnmente se emplean en la materia de Probabilidad y Estadística en el nivel superior. Se encontró que tanto las diversas temáticas abordadas en los problemas de probabilidad, las cuales no les son familiares a los estudiantes, además del lenguaje que es empleado en los textos, les causa conflicto y una inadecuada interpretación de los problemas, lo que los conduce a proponer un planteamiento erróneo en sus soluciones. El marco conceptual que soporta este estudio con respecto a la relación que existe entre el lenguaje y las matemáticas proviene de las ideas de Chomsky que subrayan la importancia de esclarecer aspectos semánticos en los problemas de matemáticas, así como las de Duval, quien pone énfasis en las representaciones y sus implicaciones para el estudio de los procesos de enseñanza y aprendizaje de las matemáticas. Con respecto a su desarrollo, este estudio tiene un enfoque social constructivista debido a los contextos que abordan los problemas con los que se trabajan.Se empleó una metodología experimental al trabajar con un grupo de 32 estudiantes quienes cursaban la materia de Probabilidad y Estadística en su tercer semestre de la carrea de Ingeniería en Sistemas Computacionales. Durante el proceso de enseñanza y aprendizaje de dicha materia, se propuso el empleo de diferentes plataformas y aplicaciones tecnológicas para tener un acercamiento diferente al usual. Tanto los estudiantes como los docentes que participaron en el estudio fueron notificados del trabajo que se realizaría, respetando su decisión en la participación de este. Primero, se aplicó un cuestionario para conocer su percepción en relación con el uso de la tecnología; después y durante un mes, se trabajó con el grupo con distintas aplicaciones tecnológicas; y finalmente, se aplicó un cuestionario de evaluación. Los resultados indic

The current definition of a conditional probability enables one to update probabilities only on the basis of stochastic information. This paper provides a definition for conditional probability with non-stochastic information. The definition is derived by a set of axioms, where the information is connected to the outcome of interest via a loss function. An illustration is presented.

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.

Principal component analysis (PCA) is a commonly used and very powerful method for simplification of multivariate data. In the presented contribution, it is, however, utilized in a novel way. The methodology described in the paper provides tables of probabilities of market strategy profitability. Values of the first two principal components are utilized in evaluation of conditional probability. The resulting probability tables can help to detect buying signals for profitable trade opportunities or can provide a warning against a high financial loss. A time period of two weeks before an actual buying signal is analyzed. After one week of holding, the share is sold and the resultant relative price differences determine the group of profitability (high profit, profit, loss, high loss). For each group, tables of occurrences and conditional probabilities are evaluated using the PCA method. The whole methodology is tested on a case study that takes in account one year long dataset (1st Aug 2014 – 14th Aug 2015) of L-3 Communications Holding shares., Hlavní analýza komponent (PCA) je běžně používaná a velmi účinná metoda pro zjednodušení vícerozměrných dat. V předkládaném příspěvku je však využívána novým způsobem. Popsaná metodika poskytuje tabulky pravděpodobnosti ziskovosti investiční strategie. Hodnoty prvních dvou hlavních složek jsou využity při vyčíslení podmíněné pravděpodobnosti. Výsledné tabulky pravděpodobnosti mohou pomoci odhalit nákupní signály pro ziskové obchodní příležitosti nebo mohou upozornit na vysokou finanční ztrátu. Celá metodika je testována na případové studii, která používá jednoroční datový soubor akcií L-3 Communications Holding (1. srpna 2014 - 14. srpna 2015).

In this piece of work we report a research from an educational point of view, whose main objective is to study the relationships between conditional and joint probabilities in the process of solving school problems. We first show how basic conditional probability problems present many difficulties to a broader sample of solvers, from compulsory and non compulsory secondary school (15- 18 years old) to future primary and secondary teachers of mathematics. We point out to the complex relationships among absolute, joint and conditional probabilities as one of the main causes of problems difficulties, and as a consequence, they depend on task variables of problems such as their mathematical structure, context in which problems are put forward and the wording used to express these probabilities. We also show how some students, while solving problems deal with conditional probabilities, not in a proper sense, but in such a way that depends on the resources or representation systems they use for solving the problem, Cet article explore la relation entre les probabilités conjointes et conditionnelles dans le processus de résolution de problèmes scolaires, du point de vue éducatif. Nous montrons tout d’abord comment des problèmes basiques en probabilité conditionnelle présentent de nombreux difficultés à un large échantillon d’étudiants, depuis les enseignants de l’enseignement secondaire jusqu’aux professeurs de mathématiques en formation. Nous signalons les rapports complexes entre les différentes probabilités impliquées dans le processus de résolution, comme l’une des principales causes de ces difficultés et comment ceux-ci dépendent des variables de la tâche associée aux problèmes, tels que sa structure mathématique, le contexte dans lequel les problèmes sont formulés et le vocabulaire utilisé pour exprimer ces probabilités dans chaque contexte. Nous montrons aussi comment certains étudiants ne parviennent pas gérer avec du bon sens les probabilités conditionnelles, mais que cela dépend des ressources ou des systèmes de représentation utilisés pour la résolution du problème, Este artigo explora a relação entre probabilidades condicionais e conjuntas no processo de resolução de problemas de escola, de uma perspectiva educacional. Primeiro mostramos como base problemas em probabilidade condicional apresenta muitas dificuldades para uma grande amostra de estudantes, desde os estudantes do ensino secundário a professores de matemática em formação. Nosso objectivo é em direcção as complexas relações entre as diferentes probabilidades envolvidas no processo de resolução como uma das principais causas de tais dificuldades e como estes dependem de variáveis da tarefa associada os problemas, tais como sua estrutura matemática, o contexto em que os problemas são formulados e o vocabulário usado para expressar essas probabilidades em cada contexto. Também mostramos como alguns alunos não conseguem usar com sentido as probabilidades condicionais, mas isso depende de recursos ou sistemas de representação utilizados para a resolução do problema, En este artículo se estudian las relaciones entre las probabilidades condicionales y conjuntas en el proceso de resolución de problemas escolares, desde una perspectiva educativa antes que cognitiva. Primero mostramos cómo los problemas básicos de probabilidad condicional presentan muchas dificultades a una amplia muestra de resolutores, desde la educación secundaria a maestros y profesores de matemáticas en formación. Apuntamos hacia las complejas relaciones entre las distintas probabilidades que participan en el proceso de resolución como una de las principales causas de dichas dificultades y cómo éstas dependen de las variables de la tarea asociadas a los problemas, tales como su estructura matemática, el contexto en el que los problemas se formulan y el vocabulario usado para expresar dichas probabilidades en cada contexto. Mostramos también cómo algunos estudiantes, cuando resuelven problemas de probabilidad condicional, no tratan con sentido las probabilidades condicionales sino que éste depende de los recursos o sistemas de representación que usan para la resolución del problema.

In this piece of work we report a research from an educational point of view, whose main objective is to study the relationships between conditional and joint probabilities in the process of solving school problems. We first show how basic conditional probability problems present many difficulties to a broader sample of solvers, from compulsory and non compulsory secondary school (15- 18 years old) to future primary and secondary teachers of mathematics. We point out to the complex relationships among absolute, joint and conditional probabilities as one of the main causes of problems difficulties, and as a consequence, they depend on task variables of problems such as their mathematical structure, context in which problems are put forward and the wording used to express these probabilities. We also show how some students, while solving problems deal with conditional probabilities, not in a proper sense, but in such a way that depends on the resources or representation systems they use for solving the problem, Cet article explore la relation entre les probabilités conjointes et conditionnelles dans le processus de résolution de problèmes scolaires, du point de vue éducatif. Nous montrons tout d’abord comment des problèmes basiques en probabilité conditionnelle présentent de nombreux difficultés à un large échantillon d’étudiants, depuis les enseignants de l’enseignement secondaire jusqu’aux professeurs de mathématiques en formation. Nous signalons les rapports complexes entre les différentes probabilités impliquées dans le processus de résolution, comme l’une des principales causes de ces difficultés et comment ceux-ci dépendent des variables de la tâche associée aux problèmes, tels que sa structure mathématique, le contexte dans lequel les problèmes sont formulés et le vocabulaire utilisé pour exprimer ces probabilités dans chaque contexte. Nous montrons aussi comment certains étudiants ne parviennent pas gérer avec du bon sens les probabilités conditionnelles, mais que cela dépend des ressources ou des systèmes de représentation utilisés pour la résolution du problème, Este artigo explora a relação entre probabilidades condicionais e conjuntas no processo de resolução de problemas de escola, de uma perspectiva educacional. Primeiro mostramos como base problemas em probabilidade condicional apresenta muitas dificuldades para uma grande amostra de estudantes, desde os estudantes do ensino secundário a professores de matemática em formação. Nosso objectivo é em direcção as complexas relações entre as diferentes probabilidades envolvidas no processo de resolução como uma das principais causas de tais dificuldades e como estes dependem de variáveis da tarefa associada os problemas, tais como sua estrutura matemática, o contexto em que os problemas são formulados e o vocabulário usado para expressar essas probabilidades em cada contexto. Também mostramos como alguns alunos não conseguem usar com sentido as probabilidades condicionais, mas isso depende de recursos ou sistemas de representação utilizados para a resolução do problema, En este artículo se estudian las relaciones entre las probabilidades condicionales y conjuntas en el proceso de resolución de problemas escolares, desde una perspectiva educativa antes que cognitiva. Primero mostramos cómo los problemas básicos de probabilidad condicional presentan muchas dificultades a una amplia muestra de resolutores, desde la educación secundaria a maestros y profesores de matemáticas en formación. Apuntamos hacia las complejas relaciones entre las distintas probabilidades que participan en el proceso de resolución como una de las principales causas de dichas dificultades y cómo éstas dependen de las variables de la tarea asociadas a los problemas, tales como su estructura matemática, el contexto en el que los problemas se formulan y el vocabulario usado para expresar dichas probabilidades en cada contexto. Mostramos también cómo algunos estudiantes, cuando resuelven problemas de probabilidad condicional, no tratan con sentido las probabilidades condicionales sino que éste depende de los recursos o sistemas de representación que usan para la resolución del problema.

In this piece of work we report a research from an educational point of view, whose main objective is to study the relationships between conditional and joint probabilities in the process of solving school problems. We first show how basic conditional probability problems present many difficulties to a broader sample of solvers, from compulsory and non compulsory secondary school (15- 18 years old) to future primary and secondary teachers of mathematics. We point out to the complex relationships among absolute, joint and conditional probabilities as one of the main causes of problems difficulties, and as a consequence, they depend on task variables of problems such as their mathematical structure, context in which problems are put forward and the wording used to express these probabilities. We also show how some students, while solving problems deal with conditional probabilities, not in a proper sense, but in such a way that depends on the resources or representation systems they use for solving the problem, Cet article explore la relation entre les probabilités conjointes et conditionnelles dans le processus de résolution de problèmes scolaires, du point de vue éducatif. Nous montrons tout d’abord comment des problèmes basiques en probabilité conditionnelle présentent de nombreux difficultés à un large échantillon d’étudiants, depuis les enseignants de l’enseignement secondaire jusqu’aux professeurs de mathématiques en formation. Nous signalons les rapports complexes entre les différentes probabilités impliquées dans le processus de résolution, comme l’une des principales causes de ces difficultés et comment ceux-ci dépendent des variables de la tâche associée aux problèmes, tels que sa structure mathématique, le contexte dans lequel les problèmes sont formulés et le vocabulaire utilisé pour exprimer ces probabilités dans chaque contexte. Nous montrons aussi comment certains étudiants ne parviennent pas gérer avec du bon sens les probabilités conditionnelles, mais que cela dépend des ressources ou des systèmes de représentation utilisés pour la résolution du problème, Este artigo explora a relação entre probabilidades condicionais e conjuntas no processo de resolução de problemas de escola, de uma perspectiva educacional. Primeiro mostramos como base problemas em probabilidade condicional apresenta muitas dificuldades para uma grande amostra de estudantes, desde os estudantes do ensino secundário a professores de matemática em formação. Nosso objectivo é em direcção as complexas relações entre as diferentes probabilidades envolvidas no processo de resolução como uma das principais causas de tais dificuldades e como estes dependem de variáveis da tarefa associada os problemas, tais como sua estrutura matemática, o contexto em que os problemas são formulados e o vocabulário usado para expressar essas probabilidades em cada contexto. Também mostramos como alguns alunos não conseguem usar com sentido as probabilidades condicionais, mas isso depende de recursos ou sistemas de representação utilizados para a resolução do problema, En este artículo se estudian las relaciones entre las probabilidades condicionales y conjuntas en el proceso de resolución de problemas escolares, desde una perspectiva educativa antes que cognitiva. Primero mostramos cómo los problemas básicos de probabilidad condicional presentan muchas dificultades a una amplia muestra de resolutores, desde la educación secundaria a maestros y profesores de matemáticas en formación. Apuntamos hacia las complejas relaciones entre las distintas probabilidades que participan en el proceso de resolución como una de las principales causas de dichas dificultades y cómo éstas dependen de las variables de la tarea asociadas a los problemas, tales como su estructura matemática, el contexto en el que los problemas se formulan y el vocabulario usado para expresar dichas probabilidades en cada contexto. Mostramos también cómo algunos estudiantes, cuando resuelven problemas de probabilidad condicional, no tratan con sentido las probabilidades condicionales sino que éste depende de los recursos o sistemas de representación que usan para la resolución del problema.

This poetry folder consists of the author’s introductory reflection followed by ten new poems that connect explicitly or implicitly to mathematics, statistics, or the teaching and learning of that content.

Very little if any previous research has been done on the potential predictability of bear and bull regimes in the Swedish stock market. In this study my aim is to predict OMXS30 bull and bear regimes with dynamic binary time series models. After using a nonparametric approach to identify the regimes of bull and bear periods in the market I looked at both an in sample and out of sample test. Based on monthly data I found different predictive variables, with the variables with highest predictive power being, the term spread and market liquidity deviation. Further variables with statistically significant results and predictive power are, the federal fund rate, purchasing manager index and the nominal return. The result can be improved using the dynamic structure in the binary response model. Using multivariate dynamic binary time series I found that the model yield higher returns than the buy and hold strategy.

Very little if any previous research has been done on the potential predictability of bear and bull regimes in the Swedish stock market. In this study my aim is to predict OMXS30 bull and bear regimes with dynamic binary time series models. After using a nonparametric approach to identify the regimes of bull and bear periods in the market I looked at both an in sample and out of sample test. Based on monthly data I found different predictive variables, with the variables with highest predictive power being, the term spread and market liquidity deviation. Further variables with statistically significant results and predictive power are, the federal fund rate, purchasing manager index and the nominal return. The result can be improved using the dynamic structure in the binary response model. Using multivariate dynamic binary time series I found that the model yield higher returns than the buy and hold strategy.

We study a natural variant of the implicational fragment of propositional logic. Its formulas are pairs of conjunctions of positive literals, related together by an implicational-like connective, the semantics of this sort of implication is defined in terms of a threshold on a conditional probability of the consequent, given the antecedent: we are dealing with what the data analysis community calls confidence of partial implications or association rules. Existing studies of redundancy among these partial implications have characterized so far only entailment from one premise and entailment from two premises. By exploiting a previously noted alternative view of this entailment in terms of linear programming duality, we characterize exactly the cases of entailment from arbitrary numbers of premises. As a result, we obtain decision algorithms of better complexity, additionally, for each potential case of entailment, we identify a critical confidence threshold and show that it is, actually, intrinsic to each set of premises and antecedent of the conclusion., Peer Reviewed, Postprint (author's final draft)

According to orthodox (Kolmogorovian) probability theory, conditional probabilities are by definition certain ratios of unconditional probabilities. As a result, orthodox conditional probabilities are regarded as undefined whenever their antecedents have zero unconditional probability. This has important ramifications for the notion of probabilistic independence. Traditionally, independence is defined in terms of unconditional probabilities (the factorization of the relevant joint unconditional probabilities). Various “equivalent” formulations of independence can be given using conditional probabilities. But these “equivalences” break down if conditional probabilities are permitted to have conditions with zero unconditional probability. We reconsider probabilistic independence in this more general setting. We argue that a less orthodox but more general (Popperian) theory of conditional probability should be used, and that much of the conventional wisdom about probabilistic independence needs to be rethought.

In order to detect and graphically visualize the absence or presence of systematic errors in fit data, conditional probabilities are employed to analyze the statistical independence or dependence of fit residuals. This concept is completely general and applicable to all scientific fields in which model parameters are fitted to experimental data. The applications presented in this work refer to published charge-density data. © 2014 International Union of Crystallography.

The paper addresses the problem of suburb development potential in the City of Belgrade. Suburbanization problem is particularly pronounced due to the rapid spreading of the city, and there is a strong initiative for extending the existing Master Plan for these suburb areas. One of the important points in that context is determination of the Land-Use suitability, as one of the foremost planning references. Herein, a statistical model is proposed for modeling of the Land-Use suitability relying on the available thematic data, including the following sources: Land-Cover, Geological, Topographic and Protected areas maps and some synthetic maps derived from these sources in a GIS environment. For the modeling purposes, the Conditional Probability approach has been implemented, using Weight of Evidence technique, in particular. Two modeling schemes have been involved: (i) model is being built and correlated in the extents of the known Land-Use suitability (training area) (ii) model is being extrapolated to the areas with partly known Land-Use suitability (by having only two particular suitability classes at disposal). Such strategy enabled optimization of classification scale, crucial for the actual comparison of the actual state and the final model, as a combination of (i) and (ii). The results were convincing and by reaching more nearly 80% of accuracy, they parallel the results of the earlier work based on different methodology. Finally, a practical applicability of the model has been also discussed.

The need for smart sustainable urban solutions has never been greater than it is today. Master plan (MP) represents rather significant document for a city development and the City of Belgrade has a tradition and long history of development under MP framework (Djuric et al 2013). Current MP (Fig. 1), made by the Urban Planning Institute of Belgrade, has been adopted in 2003 and refers to the development period by the year of 2021 (http://www.urbel.com/default.aspx?ID=uzb_BG_planovi&LN=ENG). On the other hand the City of Belgrade is facing a rapid urban sprawl so suburbanization problem is particularly pronounced. Since MP is following the current needs of the city, active subjects of economic development as well as urban municipalities, there is strong initiative to expand its current extents for the suburb areas. Among other significant points of the MP, determination of the Land-Use Suitability (LUS) is considered to be one of the foremost planning references. LUS analysis is a tool used to define future land uses or their potential. Suitability techniques enable environmental managers, planners and engineers to analyze the interactions among various factors. Decision makers and developers can use the outputs to set policies and make decisions regarding the use of land. Contemporary environmental managers and planners are aware of the technological advancements in land-use allocation and suitability modelling. New methods of spatial analysis are now commonly used in the development of land-use plans, environmental impact reviews, and site selection studies for many different land uses and public and private facilities (Collins, 2001). The earliest applications of suitability analysis conducted by engineering geologists and civil engineers for Belgrade MP area, in form of hand-drawn sieve mapping overlays was done by Šutić et al. (1972). Later, numerous researchers performed similar suitability analysis for different purposes (urban planning, defining best/optimal r

Deepening the teaching of probability in basic education, as has been recommended, underlines the importance of investigating the possibilities of teaching formal content so far not included in the curriculum, such as conditional probability and joint probability. With this purpose we study in this article the intuitive ideas of 9th grade pupils about such contents, basing on their written solutions to a task consisting of four items, two of which refer to conditional probability and the other two to joint probability. In terms of results, we note a large discrepancy in the success of the pupils in the two types of probability studied, with about two-thirds of pupils presenting correct answers to the items about conditional probability and only 6% or less of pupils presenting correct answers to the items about joint probability., O aprofundamento do ensino das Probabilidades no ensino básico, como vem sendo preconizado, releva a importância de averiguar sobre as possibilidades do ensino formal de conteúdos até agora não incluídos nos respetivos programas escolares, como é o caso da probabilidade condicionada e da probabilidade conjunta. É com esse propósito que se estudam neste artigo as ideias intuitivas de alunos do 9º ano de escolaridade nesses conteúdos a partir das suas resoluções escritas numa tarefa constituída por quatro itens, dos quais dois se referem à probabilidade condicionada e os outros dois à probabilidade conjunta. Em termos de resultados obtidos, salienta-se uma grande discrepância do sucesso dos alunos nos dois tipos de probabilidade estudados, com cerca de dois terços dos alunos a apresentarem respostas corretas nos itens de probabilidade condicionada e apenas 6% dos alunos ou menos a apresentarem respostas corretas nos itens de probabilidade conjunta.

Deepening the teaching of probability in basic education, as has been recommended, underlines the importance of investigating the possibilities of teaching formal content so far not included in the curriculum, such as conditional probability and joint probability. With this purpose we study in this article the intuitive ideas of 9th grade pupils about such contents, basing on their written solutions to a task consisting of four items, two of which refer to conditional probability and the other two to joint probability. In terms of results, we note a large discrepancy in the success of the pupils in the two types of probability studied, with about two-thirds of pupils presenting correct answers to the items about conditional probability and only 6% or less of pupils presenting correct answers to the items about joint probability., O aprofundamento do ensino das Probabilidades no ensino básico, como vem sendo preconizado, releva a importância de averiguar sobre as possibilidades do ensino formal de conteúdos até agora não incluídos nos respetivos programas escolares, como é o caso da probabilidade condicionada e da probabilidade conjunta. É com esse propósito que se estudam neste artigo as ideias intuitivas de alunos do 9º ano de escolaridade nesses conteúdos a partir das suas resoluções escritas numa tarefa constituída por quatro itens, dos quais dois se referem à probabilidade condicionada e os outros dois à probabilidade conjunta. Em termos de resultados obtidos, salienta-se uma grande discrepância do sucesso dos alunos nos dois tipos de probabilidade estudados, com cerca de dois terços dos alunos a apresentarem respostas corretas nos itens de probabilidade condicionada e apenas 6% dos alunos ou menos a apresentarem respostas corretas nos itens de probabilidade conjunta.

Deepening the teaching of probability in basic education, as has been recommended, underlines the importance of investigating the possibilities of teaching formal content so far not included in the curriculum, such as conditional probability and joint probability. With this purpose we study in this article the intuitive ideas of 9th grade pupils about such contents, basing on their written solutions to a task consisting of four items, two of which refer to conditional probability and the other two to joint probability. In terms of results, we note a large discrepancy in the success of the pupils in the two types of probability studied, with about two-thirds of pupils presenting correct answers to the items about conditional probability and only 6% or less of pupils presenting correct answers to the items about joint probability., O aprofundamento do ensino das Probabilidades no ensino básico, como vem sendo preconizado, releva a importância de averiguar sobre as possibilidades do ensino formal de conteúdos até agora não incluídos nos respetivos programas escolares, como é o caso da probabilidade condicionada e da probabilidade conjunta. É com esse propósito que se estudam neste artigo as ideias intuitivas de alunos do 9º ano de escolaridade nesses conteúdos a partir das suas resoluções escritas numa tarefa constituída por quatro itens, dos quais dois se referem à probabilidade condicionada e os outros dois à probabilidade conjunta. Em termos de resultados obtidos, salienta-se uma grande discrepância do sucesso dos alunos nos dois tipos de probabilidade estudados, com cerca de dois terços dos alunos a apresentarem respostas corretas nos itens de probabilidade condicionada e apenas 6% dos alunos ou menos a apresentarem respostas corretas nos itens de probabilidade conjunta.

The need for smart sustainable urban solutions has never been greater than it is today. Master plan (MP) represents rather significant document for a city development and the City of Belgrade has a tradition and long history of development under MP framework (Djuric et al 2013). Current MP (Fig. 1), made by the Urban Planning Institute of Belgrade, has been adopted in 2003 and refers to the development period by the year of 2021 (http://www.urbel.com/default.aspx?ID=uzb_BG_planovi&LN=ENG). On the other hand the City of Belgrade is facing a rapid urban sprawl so suburbanization problem is particularly pronounced. Since MP is following the current needs of the city, active subjects of economic development as well as urban municipalities, there is strong initiative to expand its current extents for the suburb areas. Among other significant points of the MP, determination of the Land-Use Suitability (LUS) is considered to be one of the foremost planning references. LUS analysis is a tool used to define future land uses or their potential. Suitability techniques enable environmental managers, planners and engineers to analyze the interactions among various factors. Decision makers and developers can use the outputs to set policies and make decisions regarding the use of land. Contemporary environmental managers and planners are aware of the technological advancements in land-use allocation and suitability modelling. New methods of spatial analysis are now commonly used in the development of land-use plans, environmental impact reviews, and site selection studies for many different land uses and public and private facilities (Collins, 2001). The earliest applications of suitability analysis conducted by engineering geologists and civil engineers for Belgrade MP area, in form of hand-drawn sieve mapping overlays was done by Šutić et al. (1972). Later, numerous researchers performed similar suitability analysis for different purposes (urban planning, defining best/optimal r

The paper addresses the problem of suburb development potential in the City of Belgrade. Suburbanization problem is particularly pronounced due to the rapid spreading of the city, and there is a strong initiative for extending the existing Master Plan for these suburb areas. One of the important points in that context is determination of the Land-Use suitability, as one of the foremost planning references. Herein, a statistical model is proposed for modeling of the Land-Use suitability relying on the available thematic data, including the following sources: Land-Cover, Geological, Topographic and Protected areas maps and some synthetic maps derived from these sources in a GIS environment. For the modeling purposes, the Conditional Probability approach has been implemented, using Weight of Evidence technique, in particular. Two modeling schemes have been involved: (i) model is being built and correlated in the extents of the known Land-Use suitability (training area) (ii) model is being extrapolated to the areas with partly known Land-Use suitability (by having only two particular suitability classes at disposal). Such strategy enabled optimization of classification scale, crucial for the actual comparison of the actual state and the final model, as a combination of (i) and (ii). The results were convincing and by reaching more nearly 80% of accuracy, they parallel the results of the earlier work based on different methodology. Finally, a practical applicability of the model has been also discussed.

數據挖掘的各種算法模型能否真正量化地解釋證候,關鍵是該模型所產生的有關指標能否合理地量化解釋證和一組癥狀的關系。文章以抑郁癥臨床流行病學調查數據構筑的證候隱樹模型為例子,分析了該模型所產生的互信息、累積互信息、信息覆蓋度、條件概率等幾個指標的辨證意義。認為:互信息可作為確定一組與某證有密切關聯的癥狀的依據;累積互信息和互信息數值之間的比較可以把握癥狀提供給證的信息,從而判斷癥狀的診斷價值;信息覆蓋度可以考察與某證相關聯的一組癥狀中究竟有多少癥狀、或有哪些癥狀就足可以把握該證的基本特征;而條件概率則可以通過該證關聯的一組癥狀所表現出的變化來定量地刻畫這個特征。Whether a kind of algorithm model can quantitatively interpret syndrome of traditional medicine, the key is whether some of the indexes from the model can reasonably and quantitatively the relationship between the syndrome and symptoms.And this paper take the latent tree model of sy which be constructed by the data from the depression, as an example, to analyze the differe syndrome meaning of the indexes, such as mutual information (MI) , cumulate mutual information information rate (IR) , conditional probability.Subsequently, the MI may be as a important basis for co the association between a syndrome and a group of symptoms;and meanwhile, according to the com of the MI and CMI, we could be obtained the syndrome information given by symptoms and estim diagnostic value of the symptoms.And then the IR can be used to estimate how many symptoms an symptoms, which are close relevance to one of syndrome, can be took as a confirming of the character of the syndrome, and finally, conditional probability could contribute to a better revea character of a syndrome quantitatively by the changing that a groups of symptoms relevance to a sy had happened on different condition.