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The following paper asks what forms of critical political science are possible given the advent of Machine Learning algorithms like ChatGPT? The technical conditions that provoke this question stem from the fact that such automated systems do not generate identities and representations (as do traditional media with which critical political science conventionally contends) but produce instead rendered outputs based on Bayesian probability calculations. Thus the challenge before us is to theorize criticism vis-a-vis the technical ontologies of the algorithm dispositif. [ABSTRACT FROM AUTHOR]

The Principle of Indifference ('PI': the simplest non-informative prior in Bayesian probability) has been shown to lead to paradoxes since Bertrand (1889). Von Mises (1928) introduced the 'Wine/Water Paradox' as a resonant example of a 'Bertrand paradox', which has been presented as demonstrating that the PI must be rejected. We now resolve these paradoxes using a Maximum Entropy (MaxEnt) treatment of the PI that also includes information provided by Benford's 'Law of Anomalous Numbers' (1938). We show that the PI should be understood to represent a family of informationally identical MaxEnt solutions, each solution being identified with its own explicitly justified boundary condition. In particular, our solution to the Wine/Water Paradox exploits Benford's Law to construct a non-uniform distribution representing the universal constraint of scale invariance, which is a physical consequence of the Second Law of Thermodynamics. [ABSTRACT FROM AUTHOR]

Detrital single‐grain zircon U–Pb geochronology is a powerful tool for provenance studies if information on the source rocks is available. This paper proposes a new source‐rock classification tool that uses the degree of annealing of radiation damage in detrital zircon; the annealing is expressed by the relationship between the width (full‐width at half‐maximum; FWHM) of the v3[SiO4] Raman band at ~1008 cm−1 and the calculated α‐dose. The host rocks of the zircons are classified into three types according to their emplacement process and/or thermal history: volcanic and rapidly cooled plutonic and high‐grade metamorphic rocks (type 1); rocks with hydrothermal zircons (type 2); slowly cooled igneous and metamorphic rocks (type 3). We construct a naive Bayes prediction model by training it with a collection of zircons of known types. The unknown zircons are assigned a probability of derivation from a specific host‐rock type. This classification scheme is best used as an accessory tool in provenance studies that apply detrital zircon U–Pb geochronology. [ABSTRACT FROM AUTHOR]

Many industrial products, foodstuffs and environmental samples are checked for values of different chemical parameters against tolerance limits or intervals defined in a specification or legislation. In some cases, the measured values of the different parameters are correlated due to how materials are obtained, chemical constraints and/or due to the simple fact that determinations are performed by multi-analyte procedures that share analytical operations and effects. In these cases, instead of defining an acceptance criterion for each measured value on the tested item separately based on the respective measurement uncertainty, the multivariate problem should be addressed by defining multivariate criteria. These multivariate criteria are set for a maximum total risk of wrong conformity decisions that is a complex function of all particular risks of the item being rejected or accepted by comparing each measured value with its respective limit. Computational tools have been developed to estimate the total specific risk of an item being wrongly considered to conform or not to conform with tolerance limits for various components when the measured values are independent or correlated. However, these tools must be applied for each test to check if the total specific risk is acceptable. This work presents a tool for setting multivariate acceptance limits applicable to correlated measurements and referenced to a defined total specific risk. The acceptance limits allow the decision about conformity of an item based on the simple comparison of the measured values with the acceptance limits. The acceptance limits are estimated by a user-friendly and iterative tool implemented in a MS-Excel spreadsheet and available in the . This tool is successfully applied to various conformity problems. Acceptance limits based on informative and non-informative prior information are compared for a critical review of the merits and problems associated with Bayesian or frequentist conformity assessments. [ABSTRACT FROM AUTHOR]

Absolute tectonic stress estimation is of fundamental importance to investigating regional earthquake hazards, while traditional algorithms only invert relative stress and its orientations. We use topography data and rupture models to invert the absolute tectonic stress in the source region of the 2016 Mw 7.0 Kumamoto earthquake. We adopt a basic assumption that the slip direction of each sub‐fault is in the shear direction of the total stress tensor, which is a combination of tectonic and topographic stresses. We use the Bayesian theorem to describe the probability density function of stress parameters and perform a grid search for the optimal values and uncertainties of stress parameters. Our synthetic test shows that the true parameters can be reliably estimated over a broad range of tectonic stress magnitudes. In the application to the Kumamoto earthquake, we find that the optimal tectonic stress is uniaxial tensional with a magnitude of 35 MPa and orientation of N6.5°W, which is at an angle of ∼47.5° to the fault strike. The average total shear stress on the fault plane is 16 MPa on the fault plane. With the inverted stress parameters, we evaluate the pore pressure based on the absolute stress field, the result of which is within a range of 0.3–0.45 of the lithostatic stress. Plain Language Summary: We estimate the absolute stress of the 2016 Kumamoto earthquake source region using rupture models and ground topography. We assume the overall slip direction is controlled by the tectonic stress loading, while the topographic stresses introduce spatial variation of slip directions. The tectonic stress field in the Kumamoto source region is estimated as uniaxial‐tensional stress of about 35 Mpa close the north‐south direction. This estimation is slightly higher than the previous estimations using other assumptions. Key Points: We develop an algorithm to invert for the absolute tectonic stress in the Kumamoto earthquake regionWe obtain a tectonic stress in uniaxial tension with magnitude 35 MPa and orientation N6.5°WWe estimate that the pore‐pressure above 5 km depth is about 0.3–0.45 times lithostatic stress, which is close to a drained system [ABSTRACT FROM AUTHOR]

Educational researchers advocate the use of an effect size and its confidence interval to assess the effectiveness of interventions instead of relying on a p-value, which has been blamed for lack of reproducibility of research findings and the misuse of statistics. The aim of this study is to provide a framework, which can provide direct evidence of whether an intervention works for the study participants in an educational trial as the first step before generalizing evidence to the wider population. A hierarchical Bayesian model was applied to ten cluster and multisite educational trials funded by the Education Endowment Foundation in England, to estimate the effect size and associated credible intervals. The use of posterior probability is proposed as an alternative to p-values as a simple and easily interpretable metric of whether an intervention worked or not. The probability of at least one month's progression or any other appropriate threshold is proposed to use in education outcomes instead of using a threshold of zero to determine a positive impact. The results show that the probability of at least one month's progress ranges from 0.09 for one trial, GraphoGame Rime, to 0.94 for another, the Improving Numeracy and Literacy trial. [ABSTRACT FROM AUTHOR]

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics. [ABSTRACT FROM AUTHOR]

Background: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.Methods: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed.Results: To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively.Conclusions: The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging. [ABSTRACT FROM AUTHOR]

Circuit breakers (CBs) are of vital importance for the stability of power systems, and a new two‐stage hierarchical state estimation method is proposed for 500 kV sulphur hexafluoride CBs based on Bayesian probability and perceptron neural networks. On the basis of the samples collected from Yunnan Power Grid in China, a new indicator system is constructed by association rules. Bayesian probability is applied to measure the correlation between the individual indicators and comprehensive indicators at the same status level, to weigh the individual indicators. Also, an adaptive perceptron is improved to train the weights of comprehensive indicators in different operational conditions, to eliminate the influence of the imbalance problem of relative deterioration. Then, the operating state of equipment can be inferred according to the calculated comprehensive scores. Finally, taking the actual operating equipment as an example, the effectiveness of this proposed method is proved by sample tests and comparison with other existing linear methods. [ABSTRACT FROM AUTHOR]

• Probability analysis of reliability of steel bridge members exposed to fatigue. • Probabilistic design of bridge inspections focused on fatigue damage. • Contrast-based global sensitivity analysis of failure probability P f. • Identification of crucial effects of equivalent stress range and initial crack length on P f. • Identification of crucial effects of interactions among input variables on small P f values. The article deals with the analysis of the probability of failure of a load-bearing steel bridge member under bending. The focus is on fatigue failure caused by stress cycles from multiple repeated traffic loading on the bridge. Failure is defined by the occurrence of a fatigue crack of critical size. Crack propagation and the fatigue limit state are described using linear fracture mechanics. The failure probability is a function of the equivalent stress range, initial crack length, Paris exponent, number of load cycles (stress changes) increasing over time and other input random variables. The failure probability is evaluated in time steps and then studied using a new type of global sensitivity analysis subordinated to contrasts. The results of the sensitivity analysis show that the first (second) dominant variable is the equivalent stress range (initial crack length) at any given point in time of the bridge operation. Strong main effect of equivalent stress range is associated with higher values of failure probability at the end of the lifetime of the bridge. Small values of failure probability are strongly influenced by interactions among input variables, which cannot be expressed as the sum of main effects of the individual input variables. The main and higher-order indices of each input variable are supplemented by displaying its total index. The direct goal of probability and sensitivity analysis is structural reliability. Sensitivity analysis confirms and deepens the knowledge gained from the time-dependent probability analysis. The numerical example illustrates the rationality of probability-oriented sensitivity indices and the feasibility of their estimation using Latin Hypercube Sampling (LHS). In addition, structural reliability is studied using Bayesian probability, which identifies the times for planning bridge inspections. [ABSTRACT FROM AUTHOR]

Recently, the diverse virtual reality devices are developed and utilized. Particularly, the devices that recognize the motions of users such as griping hands and opening hands are issued to utilize the motions of the users as input methodology. Traditional research about motion recognition suggests user's motion estimation methods by calculating Bayesian probability after measuring the orientation of the motions by a Myo, which is one of contact-type motion recognition devices. However, the motion estimation methods have the problem of low motion estimation accuracy, given that orientation is defined by x, y, and z, which are calculated separately only considering the values of the corresponding axis. In order to improve motion estimation accuracy, motions should be estimated by considering the values of all axis. This paper proposes a method using genetic algorithm to calculate weights, which are applied to estimate motions through Bayesian probability by considering the values of all axis after measuring user's motions with a Myo. The proposed method consists of three steps. First, the Bayesian probability is calculated by considering the correlations of x, y, and z of the orientation of a Myo. Second, weights are determined by applying genetic algorithm. Third, motions are estimated through the Bayesian probability with the determined weights. Experiments were conducted to compare the Bayesian probability between the traditional method based on min/max and the proposed method, which showed that the proposed method had reduced the difference of the orientations by 32%. [ABSTRACT FROM AUTHOR]

Liver segmentation and volumetry in native MR-volume data is an important topic in epidemiological research. Manual liver segmentation is extremely time-consuming and often infeasible requiring automatized methods. Automatic liver segmentation is challenging because of the large variability in liver shape and appearance and the low contrast to neighboring organs. We present a fully automatized liver segmentation framework that uses a sequence of modules based on individualized model knowledge on liver appearance and shape. Liver probability maps are computed that incorporate organ-specific features like MR-intensity distributions, inner-organ MR-differences and liver positions. Probability map generation differentiates automatically between fatty and non-fatty livers. Moreover, we improve an existing technique for prior shape level set segmentation to delineate the liver in tissue-specific liver probability maps and to recognize cystic hepatic tissue. Dice coefficients of 0.937 and low volumetric errors on 35 test data sets confirm the robust segmentation quality of the framework. [ABSTRACT FROM AUTHOR]

There are diverse types of devices based on natural user interface/experience for humanized computing. One such device, the MYO allows the measurement of arm motions and uses them as an interface based on gestures. There are several research works for measuring the arm motions using MYOs. For example, one of the studies defines two types of motions for a forearm and for an upper arm, respectively. The orientations of the two types are measured by two MYOs. Bayesian probabilities are calculated based on the measured orientations and are utilized to estimate the orientations of the upper arm that is not being measured. However, because the orientation of the MYO can be expressed by one quaternion, the Bayesian probability by quaternions is more accurate than the Bayesian probability by each element of quaternions. This paper proposes a motion estimation method to increase the accuracy of motion estimation. The orientations obtained from MYO are expressed by one quaternion and are clustered by K-means. In the experiments, the performance of the proposed method was validated by analyzing the difference between estimated motion quaternions and measured motion quaternions, which showed enhanced performance. [ABSTRACT FROM AUTHOR]

Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%. [ABSTRACT FROM AUTHOR]

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC.We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method. [ABSTRACT FROM AUTHOR]

Background: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. Results: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. Conclusions: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations. [ABSTRACT FROM AUTHOR]

There are a variety of dangerous situations that elementary school students encounter when they commute to their school. Given that smart phone is the one of devises utilized a lot by the elementary school students, it is possible to develop and utilize the safety apps of the smart phone for the elementary school students to protect them from the variety of the dangerous situations. For an example, when the elementary school students encounter dangerous situations in cloud computing environments, the urgent situations can be notified to their parents automatically and the smart phones of theirs can inter-perform with diverse kinds of deployed sensors and actuators. One of research introduces an app that utilizes behavior networks. By applying behavior network two times for recognizing dangerous situations, two different situations can be recognized and handled separately. However, given that the dangerous situations are more complicated, further research is required to improve the processes of the app. This paper proposes a multi-level behavior network-designed method to automatically determine dangerous situations. Behavior network is applicable to the circumstances by using measured values of smart phone sensors. To recognize dangerous situations by utilizing behavior networks, Bayesian probability is also utilized. By learning dangerous situations iteratively, multiple dangerous situations were recognized and handled accurately, which increases the safety of elementary school students. [ABSTRACT FROM AUTHOR]

We examine the causal relationship between domestic violence and themurder of a woman by her male partner using conditional independence. Given a murder, we consider the independence between domestic violence and a murdering husband/partner. The tria l of O.J. Simpson illustrates the usage of conditional independence in the courtroom. We extend Good's Bayesian analysis to include concepts of conditional independence and estimate related probabilities with timely data.We argue that evidence of domestic violence is relevant only where wives who suffer spousal abuse have a higher probability of being murdered by their spouses than non-abused wives. [ABSTRACT FROM AUTHOR]

Dicofol application may be an important source to elevate the dichlorodiphenyltrichloroethane (DDT) residues to soils in Fujian, Southeast China, after the technical DDT was banned, which left DDT residues from the historical application. The DDT residues varied geographically, corresponding to the varied potential sources of DDT. In this study, a novel approach based on the Bayesian method (BM) was developed to identify the source contributions of DDT to soils, composed with both historical DDT and dicofol. The Naive Bayesian classifier was used basing on the subset of the samples, which were determined by chemical analysis independent of the Bayesian approach. The results show that BM (95%) was higher than that using the ratio of o, p ′-/ p, p ′-DDT (84%) to identify DDT source contributions. High detection rate (97%) of dicofol ( p, p ′-OH-DDT) was observed in the subset, showing dicofol application influenced the DDX levels in soils in Fujian. However, the contribution from historical technical DDT source was greater than that from dicofol in Fujian, indicating historical technical DDT was still an important pollution source to soils. In addition, both the DDX (DDT isomers and derivatives) level and dicofol contribution in non-agricultural soils were higher than other agricultural land uses, especially in hilly regions, the potential cause may be the atmospheric transport of dicofol type DDT, after spraying during daytime, or regional difference on production and application. [ABSTRACT FROM AUTHOR]

A size-age modelling technique is presented for assessing the vital rates, stock and recruitment of eel populations in semi-closed lagoons with fully monitored migration of silver eels. Data for yellow and silver European eels ( Anguilla anguilla L.) were obtained in 2011 from the Comacchio lagoon (Italy). The analysis was performed in three steps: (i) correction of yellow eel data, which are affected by the fyke nets selectivity during samplings, (ii) estimation of survival curve, stock, recruitment and metamorphosis rates of the population (calibration using data from 2011) and (iii) validation of the model using the observed amount of silver eel migrating population of the next year. A bootstrap procedure was used to assess the level of uncertainty for each parameter using the 95% intervals of the highest posterior density distribution HPDD (Bayesian approach). The measured abundance of silver eels was 0.56 ind·ha−1, while the yellow eel abundance and recruitment were estimated by the model for 2011 at 8.77 ind·ha−1 and 5.99 ind. ha−1 respectively. The model performance during validation was satisfactory as the observed total mass of migrating population of 2012 (3777 kg) was inside the 95% HPDD intervals (3197-3839 kg) of model's predictions. The estimated stocks and recruitment were at least ten times lower from the respective estimations of previous studies of 1989 highlighting the crucial conditions of the population. The proposed modelling approach can provide significant information about eel population conditions, facilitating the evaluation of a range of management options in the context of eel conservation plans. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technique is a popular tool for materials characterization and non-destructive testing. Originating from the stochastic motion of defects in solids, AE is a random process by nature. The challenging problem arises whenever an attempt is made to identify specific points corresponding to the changes in the trends in the fluctuating AE time series. A general Bayesian framework is proposed for the analysis of AE time series, aiming at automated finding the breakpoints signaling a crossover in the dynamics of underlying AE sources. [ABSTRACT FROM AUTHOR]

Given that entropy-based IT technology has been applied in homes, office buildings and elsewhere for IT security systems, diverse kinds of intelligent services are currently provided. In particular, IT security systems have become more robust and varied. However, access control systems still depend on tags held by building entrants. Since tags can be obtained by intruders, an approach to counter the disadvantages of tags is required. For example, it is possible to track the movement of tags in intelligent buildings in order to detect intruders. Therefore, each tag owner can be judged by analyzing the movements of their tags. This paper proposes a security approach based on the received signal strength indicators (RSSIs) of beacon-based tags to detect intruders. The normal RSSI patterns of moving entrants are obtained and analyzed. Intruders can be detected when abnormal RSSIs are measured in comparison to normal RSSI patterns. In the experiments, one normal and one abnormal scenario are defined for collecting the RSSIs of a Bluetooth-based beacon in order to validate the proposed method. When the RSSIs of both scenarios are compared to pre-collected RSSIs, the RSSIs of the abnormal scenario are about 61% more different compared to the RSSIs of the normal scenario. Therefore, intruders in buildings can be detected by considering RSSI differences. [ABSTRACT FROM AUTHOR]

The issue of feature point mismatching among infrared image sequence would bring big challenge to estimating the relative motion of non-cooperative spacecraft for it couldn’t provide the prior knowledge about its geometric structure and motion pattern. The paper introduces particle filter to precisely match the feature points within a desired region predicted by a kinetic equation, and presents a least square estimation-based algorithm to measure the relative rolling motion of non-cooperative spacecraft. The state transition equation and the measurement update equation of non-cooperative spacecraft are represented by establishing its kinetic equations, and then the relative pose measurement is converted to the maximum posteriori probability estimation via assuming the uncertainties about geometric structure and motion pattern as random and time-varying variables. These uncertainties would be interpreted and even solved through continuously measuring the image feature points of the rotating non-cooperative infrared spacecraft. Subsequently, the feature point is matched within a predicted region among sequence infrared image using particle filter algorithm to overcome the position estimation noise caused by the uncertainties of geometric structure and motion pattern. Finally, the position parameters including rotation motion are estimated by means of solving the minimum error of feature point mismatching using least square estimate theory. Both simulated and real infrared image sequences are induced in the experiment to evaluate the performance of the relative rolling estimation, and the experimental data show that the rolling motion estimated by the proposed algorithm is more robust to the feature extraction noise and various rotation speed. Meanwhile, the relative rolling estimation error would increase dramatically with distance and rotation speed increasing. [ABSTRACT FROM AUTHOR]

This article describes reasoning strategies used by clinicians in different diagnostic circumstances and how these modes of inquiry may allow further insight into the evaluation and treatment of patients. Specifically, it aims to make explicit the implicit logical considerations that guide a variety of strategies in the diagnostic process, as exemplified in specific clinical cases. It focuses, in particular, in strategies that clinicians use to move from a large set of possible diagnoses initially suggested by abductive inferences - the process of hypothesis generation that creates a diagnostic space - to a narrower set or even to a single 'best' diagnosis, where the criteria to determine what is 'best' may differ according to different strategies. Experienced clinicians should have a diversified kit of strategies - for example, Bayesian probability or inference to a lovely explanation - to select from among previously generated hypotheses, rather than rely on any one approach every time. [ABSTRACT FROM AUTHOR]

The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) and multidrug-resistant TB (MDR-TB) are major health problems in Western Province, Papua New Guinea. While comprehensive expansion of TB control programs is desirable, logistical challenges are considerable, and there is substantial uncertainty regarding the true disease burden. We parameterized our previously described mathematical model of Mycobacterium tuberculosis dynamics in Western Province, following an epide-miologic assessment. Five hypothetical scenarios representing alternative programmatic approaches during the period from 2013 to 2023 were developed with local staff. Bayesian uncertainty analyses were undertaken to explicitly acknowledge the uncertainty around key epidemiologic parameters, and an economic evaluation was performed. With continuation of existing programmatic strategies, overall TB incidence remained stable at 555 cases per 100,000 population per year (95% simulation interval (SI): 420, 807), but the proportion of incident cases attributable to MDR-TB increased from 16% to 35%. Comprehensive, provincewide strengthening of existing programs reduced incidence to 353 cases per 100,000 population peryear (95% SI: 246,558), with 46% being cases of MDR-TB, while incorporating programmatic management of MDR-TB into these programs reduced incidence to 233 cases per 100,000 population per year (95% SI: 198, 269) with 14% MDR-TB. Most economic costs were due to hospitalization during the intensive treatment phase. Broad scale-up of TB control activities in Western Province with incorporation of programmatic management of MDR-TB is vital if control is to be achieved. Community-based treatment approaches are important to reduce the associated economic costs. [ABSTRACT FROM AUTHOR]

ABSTRACT The Bayesian perspective on historiography is commonsensical: If historiography is not certain like a priori knowledge or sense data, and it is not fiction, historiography is probable. Richard Carrier's book argues for a Bayesian, probabilistic interpretation of historiography in general and of the debate about the historicity of Jesus in particular. Jesus can be interpreted as a historically transmitted reference of 'Jesus,' as a bundle of properties, or literally. Carrier devotes too much energy to debating literalism that confuses evidence with hypotheses. But evidence preserves information to different degrees; it is true or not. Carrier proposes to apply objective, frequentist Bayesianism in historiography despite the difficulties in assigning values. He argues that ranges of values can determine historiographical hypotheses. Carrier does not analyze in Bayesian terms the main method for Bayesian determination of posterior probabilities in historiography: inference from multiple independent sources. When the prior probability of a hypothesis is low, but at least two independent evidential sources, such as testimonies, support it, however unreliable each of the testimonies is, the posterior probability leaps. The problem with the Synoptic Gospels as evidence for a historical Jesus from a Bayesian perspective is that the evidence that coheres does not seem to be independent, whereas the evidence that is independent does not seem to cohere. Carrier's explanation of some the evidence in the Gospels is fascinating as the first Bayesian reconstruction of structuralism and mimesis. Historians attempted to use theories about the transmission and preservation of information to find more reliable parts of the Gospels, parts that are more likely to have preserved older information. Carrier is too dismissive of such methods because he is focused on hypotheses about the historical Jesus rather than on the best explanations of the evidence. I leave open questions about the degree of scholarly consensus and the possible reasons for it. [ABSTRACT FROM AUTHOR]

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela. [ABSTRACT FROM AUTHOR]

Organ segmentation in magnetic resonance (MR) volume data is of increasing interest in epidemiological studies and clinical practice. Especially in large-scale population-based studies, organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time consuming and prone to reader variability, large-scale studies need automatic methods to perform organ segmentation. In this paper, we present an automated framework for renal tissue segmentation that computes renal parenchyma, cortex, and medulla volumetry in native MR volume data without any user interaction. We introduce a novel strategy of subject-specific probability map computation for renal tissue types, which takes inter- and intra-MR-intensity variability into account. Several kinds of tissue-related 2-D and 3-D prior-shape knowledge are incorporated in modularized framework parts to segment renal parenchyma in a final level set segmentation strategy. Subject-specific probabilities for medulla and cortex tissue are applied in a fuzzy clustering technique to delineate cortex and medulla tissue inside segmented parenchyma regions. The novel subject-specific computation approach provides clearly improved tissue probability map quality than existing methods. Comparing to existing methods, the framework provides improved results for parenchyma segmentation. Furthermore, cortex and medulla segmentation qualities are very promising but cannot be compared to existing methods since state-of-the art methods for automated cortex and medulla segmentation in native MR volume data are still missing. [ABSTRACT FROM PUBLISHER]

With an accidental release of an airborne pollutant, it is always critical to know where, when, and how the pollutant has been released. Then, emergency measures can be scientifically advised to prevent any possible harm. This investigation proposes an inverse model to identify the release location, the temporal rate profile, and the sensor alarming time from the start of a pollutant release. The first step is to implement the inverse operation to the cause-effect matrix to obtain the release rate profiles for discrete candidate scenarios with concentration information provided by one sensor. The second step is to interpret the occurrence probability of each solution in the first step with the Bayesian model by matching the concentration at the other sensor. The proposed model was applied to identify a single pollutant source in a two-dimensional enclosure using measurement data and in a three-dimensional aircraft cabin with simulated data. The results show that the model is able to correctly determine the pollutant source location, the temporal rate profile, and the sensor alarming time. The known conditions for input into the inverse model include a steady flow field and the valid temporal concentrations at two different locations. [ABSTRACT FROM AUTHOR]

What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach. [ABSTRACT FROM AUTHOR]

Exact analysis of glottal vibration patterns is indispensable for the assessment of laryngeal pathologies. Increasing demand of voice related examination and large amount of data provided by high-speed laryngoscopy and stroboscopy call for automatic assistance in research and patient care. Automatic glottis segmentation is necessary to assist glottal vibration pattern analysis, but unfortunately proves to be very challenging. Previous glottis segmentation approaches hardly consider characteristic glottis features as well as inhomogeneity of glottal regions and show serious drawbacks in their application for diagnostic purposes. We developed a fully automated glottis segmentation framework that extracts a set of glottal regions in endoscopic videos by using a flexible thresholding technique combined with a refining level set method that incorporates prior glottis shape knowledge. A novel descriptor for glottal regions is presented to remove potential nonglottal fake regions that show glottis-like shape properties. Knowledge of local color distributions is incorporated into Bayesian probability image generation. Glottal regions are then tracked frame-by-frame in probability images with a region-based level set segmentation strategy. Principal component analysis of pixel coordinates is applied to determine glottal orientation in each frame and to remove nonglottal regions if erroneous regions are included. The framework shows very promising results concerning segmentation accuracy and processing times and is applicable for both stroboscopic and high-speed videos. [ABSTRACT FROM AUTHOR]

Collaborative filtering is the critical technology for Internet recommendation system. In order to deal with the precision, efficiency and the scalability of data set in the recommendation algorithm, this paper proposed a multi-feature collaborative filtering algorithm with grey correlation similarity based on Hadoop architecture. With the analytic result of users' features based on Bayesian probability model, it formed the effective nearest neighbors' set. It built the prediction score matrix with the MapReduce model of Hadoop. At last, it formed a recommendation list with the grey correlation similarity model based on the efective nearest neighbors' set and the prediction score matrix. It improved experimental results show that the efficiency and precision of recommendation. And this algorithm is able to deal with the large-scale data set. [ABSTRACT FROM AUTHOR]

In the Structural Health Monitoring of bridges, the effects of the operational and environmental variability on the structural responses have posed several challenges for early damage detection. In order to overcome those challenges, in the last decade recourse has been made to the statistical pattern recognition paradigm based on vibration data from long-term monitoring. This paradigm has been characterized by the use of purely data-based algorithms that do not depend on the physical descriptions of the structures. However, one drawback of this procedure is how to set up the baseline condition for new and existing bridges. Therefore, this paper proposes an algorithm with a Bayesian approach based on a Markov-chain Monte Carlo method to cluster structural responses of the bridges into a reduced number of global state conditions, by taking into account eventual multimodality and heterogeneity of the data distribution. This approach stands as an improvement over the classical maximum likelihood estimation based on the expectation–maximization algorithm. Along with the Mahalanobis squared-distance, this approach permits one to form an algorithm able to detect structural damage based on daily response data even under abnormal events caused by temperature variability. The applicability of this approach is demonstrated on standard data sets from a real-world bridge in Switzerland, namely the Z-24 Bridge. The analysis suggests that this algorithm might be useful for bridge applications because it permits one to overcome some of the limitations posed by the pattern recognition paradigm, especially when dealing with limited amounts of training data and/or data with nonlinear temperature dependency. [ABSTRACT FROM AUTHOR]

The advent of multiple Global Navigation Satellite System (GNSS) constellations will result in a considerable increase in the number of satellites for positioning worldwide. This substantial improvement in measurement redundancy has the potential to radically advance receiver autonomous integrity monitoring (RAIM) performance. However, regardless of the number of satellites, the performance of existing RAIM methods is sensitive to the assumed prior probabilities of individual fault hypotheses. In this paper, a new method is developed using Bayes’ theorem to generate upper bounds on posterior probabilities of individual fault hypotheses given current user measurements. These bounds are used in a Bayesian fault-tolerant position estimator (FTE) that minimizes integrity risk. The detection test statistic is a measurement-based integrity risk bound, which is directly compared with a pre-specified risk requirement. The associated challenge of quantifying continuity risk is resolved using a bounding approach, which is also detailed in this work. The new Bayesian FTE method is shown to be more robust to uncertainty in prior probability of fault occurrence than existing RAIM methods. [ABSTRACT FROM AUTHOR]

Building on the work of Lawvere and others, we develop a categorical framework for Bayesian probability. This foundation will then allow for Bayesian representations of uncertainty to be integrated into other categorical modeling applications. The main result uses an existence theorem for regular conditional probabilities by Faden, which holds in more generality than the standard setting of Polish spaces. This more general setting is advantageous, as it allows for non-trivial decision rules (Eilenberg-Moore algebras) on finite (as well as non finite) spaces. In this way, we obtain a common framework for decision theory and Bayesian probability. [ABSTRACT FROM AUTHOR]

Background: Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework. Methods: Our dendritic tree extraction (DTE) method uses small amounts of labelled training data on MIPs to learn noise models of texture-based features from the responses of tree structures and image background. Our strategy lies in evaluating statistical models of noise that account for both the variability generated from the imaging process and from the aggregation of information in the MIP images. These noisy models are then used within a probabilistic, or Bayesian framework to provide a coarse 2D dendritic tree segmentation. Finally, some post-processing is applied to refine the segmentations and provide skeletonized trees using a morphological thinning process. Results: Following a Leave-One-Out Cross Validation (LOOCV) method for an MIP databse with available “ground truth” images, we demonstrate that our approach provides significant improvements in tree-structure segmentations over traditional intensity-based methods. Improvements for MIPs under various imaging conditions are both qualitative and quantitative, as measured from Receiver Operator Characteristic (ROC) curves and the yield and error rates in the final segmentations. In a final step, we demonstrate our DTE approach on previously unseen MIP samples including the extraction of skeletonized structures, and compare our method to a state-of-the art dendritic tree tracing software. Conclusions: Overall, our DTE method allows for robust dendritic tree segmentations in noisy MIPs, outperforming traditional intensity-based methods. Such approach provides a useable segmentation framework, ultimately delivering a speed-up for dendritic tree identification on the user end and a reliable first step towards further morphological characterizations of tree arborization. [ABSTRACT FROM AUTHOR]

Bayesian probability calculus has recently emerged as a model for how the mind learns about fictional and cultural environments. This essay considers reading a narrative as a process of learning about the probabilities of the fictional world and explores two novels about Quixotic readers from a Bayesian perspective. The juxtaposition of the fictional and the real, which the standard understanding of the Quixote relies on, is replaced by an outline of the dynamics of the learning process in Quixotes who already know 'the rule', that is, the probabilities of their fictional environment, but need to use this knowledge with discernment. The conclusion draws connexions between the Quixotic learning process and the ways in which fiction contributes to human mental, social and cultural development. [ABSTRACT FROM AUTHOR]

The idea that one can possibly develop computational models that predict the emergence, growth, or decline of tumors in living tissue is enormously intriguing as such predictions could revolutionize medicine and bring a new paradigm into the treatment and prevention of a class of the deadliest maladies affecting humankind. But at the heart of this subject is the notion of predictability itself, the ambiguity involved in selecting and implementing effective models, and the acquisition of relevant data, all factors that contribute to the difficulty of predicting such complex events as tumor growth with quantifiable uncertainty. In this work, we attempt to lay out a framework, based on Bayesian probability, for systematically addressing the questions of Validation, the process of investigating the accuracy with which a mathematical model is able to reproduce particular physical events, and Uncertainty quantification, developing measures of the degree of confidence with which a computer model predicts particular quantities of interest. For illustrative purposes, we exercise the process using virtual data for models of tumor growth based on diffuse-interface theories of mixtures utilizing virtual data. [ABSTRACT FROM AUTHOR]

“Damned by faint praise” is the phenomenon whereby weak positive information leads to a negative change in belief. This seemingly conflicts with normative Bayesian predictions, which prescribe that positive information should only exert a positive change in belief. We argue that the negative belief change is due to an inference from critical missing evidence; that is, an implicit argument from ignorance. Such an inference is readily incorporated within a version of Bayes’ theorem incorporating the concept of epistemic closure. This reformalisation provides a general theoretical framework for the phenomenon that clearly outlines those conditions under which it should be observed, and its conceptual relationship with other argumentation phenomena. [ABSTRACT FROM AUTHOR]

Bayesian probability has recently been proposed as a normative theory of argumentation. In this article, we provide a Bayesian formalisation of the ad Hitlerum argument, as a special case of the ad hominem argument. Across three experiments, we demonstrate that people's evaluation of the argument is sensitive to probabilistic factors deemed relevant on a Bayesian formalisation. Moreover, we provide the first parameter-free quantitative evidence in favour of the Bayesian approach to argumentation. Quantitative Bayesian prescriptions were derived from participants' stated subjective probabilities (Experiments 1 and 2), as well as from frequency information explicitly provided in the experiment (Experiment 3). Participants' stated evaluations of the convincingness of the argument were well matched to these prescriptions. [ABSTRACT FROM AUTHOR]

Fully automatic 3-D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3-D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects, and similar tissue properties of adjacent tissues. We developed a 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation. The probability map quality is improved in a multistep refinement approach. An extended prior shape level set segmentation method is then applied on the refined probability maps. The segmentation quality is improved by incorporating an exterior cortex edge alignment technique using cortex probability maps. In contrast to previous approaches, we combine several relevant kidney parenchyma features in a sequence of segmentation techniques for successful parenchyma delineation on native MR datasets. Furthermore, the proposed method is able to recognize and exclude parenchymal cysts from the parenchymal volume. We analyzed four different quality measures showing better results for right parenchymal tissue than for left parenchymal tissue due to an incorporated liver part removal in the segmentation framework. The results show that the outer cortex edge alignment approach successfully improves the quality measures. [ABSTRACT FROM AUTHOR]

Abstract: In 2003, birds similar to the extinct New Zealand storm-petrel Oceanites maorianus were observed in Hauraki Gulf NZ, raising the possibility of rediscovery after 150years. O. maorianus has and continues to be surrounded by taxonomic uncertainty, being variously described as a distinct genus, a distinct species, or merely a plumage variant. This uncertainty has hindered conservation planning and funding for the species. Here we examine the taxonomic identity of the rediscovered birds and museum specimens using phylogenetic analysis of mitochondrial (1143bp cytochrome b) and nuclear (890bp β-fibrinogen) genes. Using cytochrome b sequence amplified from the 150+ year old specimens, we found that the extant and museum O. maorianus were the same taxon (0.01 genetic distance), with both differing from all other storm-petrel taxa. Using both genes, we examined the phylogenetic affinities of O. maorianus to the Oceanitinae and Hydrobatinae storm-petrels finding that O. maorianus was more closely aligned to Fregetta (0.08–0.09) than Oceanites (0.11–0.12), thereby confirming its status as a distinct taxon, not a plumage variant of O. oceanicus. Our analysis verifies that the previously presumed extinct New Zealand storm-petrel has been rediscovered and can now be assigned a conservation priority commensurate with its critically endangered status. [Copyright &y& Elsevier]

This paper draws together four perspectives that contribute to a new understanding of probability and solving problems involving probability. The first is the Subjective Bayesian perspective that probability is affected by one's knowledge, and that it is updated as one's knowledge changes. The main criticism of the Bayesian perspective is the problem of assigning prior probabilities; this problem disappears with our Information Theory perspective, in which we take the bold new step of equating probability with information. The main point of the paper is that the formal perspective (formalize, calculate, unformalize) is beneficial to solving probability problems. And finally, the programmer's perspective provides us with a suitable formalism. To illustrate the benefits of these perspectives, we completely solve the hitherto open problem of the two envelopes. [ABSTRACT FROM AUTHOR]