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Delving into the review reports, this paper is aimed at analyzing reviewers' attitudes toward different sections of the manuscripts they review. The research focuses on the consistency of reviewers' evaluation through analysis of their assessment of separate parts of a paper, if it corresponds with the recommendations they made to the editors and whether a paper needs revision or should be accepted/rejected. It is assumed that the assessment of separate parts of a paper should be consistent with the final decision regarding the acceptance or rejection of a manuscript. Based on the analysis presented in this paper it can be concluded that the assessments of separate parts of articles in the evaluation sheets do not fully reflect the final recommendations of the reviewers. The results showed that the most correlated and therefore the most significant sections for the reviewers are the main text and the conclusions. The conditional probability analysis showed that the decision of reviewers, when number of points in the evaluation sheet is taken into consideration, is slightly unpredictable. No significant differences in the reviewers' recommendations based on gender or country of origin of the reviewers were found. [ABSTRACT FROM AUTHOR]

The obligated implementation of the Electronic Chart Display and Information System (ECDIS) began ten years ago, and during this time, we could observe different types of familiarization with the system through its users. The incorrect use of the ECDIS is a problem recognized by the International Hydrographic Organization (IHO), and it is caused by disparate levels of education and the quality of courses. Another aspect of the recognized problem is the unequal length of the ECDIS specific course training period through which users acquire type-specific familiarization with the system. Switching between different types of ECDISs makes users more prone to errors until they become fully familiar with the new product system. This paper analyzes seafarers' knowledge and ECDIS usage over a 6-year period. Based on obtained data, models that enable risk analysis based on conditional probability were created. This paper presents the use of Bayesian modeling to reduce errors in maritime accidents caused by inadequate use of the ECDIS. [ABSTRACT FROM AUTHOR]

In this paper, we develop a personal synthesis of the most outstanding research on the teaching and learning of probability in the past years. We conducted a systematic search to examine publications on this topic in mathematics education, statistics education, education, and psychology journals. This exploration was complemented by additional studies published in conference proceedings or books. We classified these papers to highlight the main recent research tendencies in the field, according to the theme studied and considering the research objectives. Epistemological analyses suggest that informal inference based on simulation diminishes the topic abstraction but reduces probability to its frequentist view. Topics receiving particular attention include children's probabilistic knowledge, the effect of visualizations on solving conditional probability problems, teachers' education and probability modelling. In the final section, we recommend relevant points in which more investigation is needed to complete our knowledge about teaching and learning. In particular, we miss research on teachers' mathematical knowledge of many probability concepts and on their didactic knowledge. [ABSTRACT FROM AUTHOR]

The feature information of small-scale targets is seriously missing under the interference of complex underwater terrain and light refraction. Moreover, the unbalanced distribution of underwater target samples can also affect the accuracy of spatial semantic feature extraction. Aiming at the above problems, this paper proposes a dynamic multiscale feature fusion method for underwater target recognition. Firstly, this paper uses multiscale info noise contrastive estimation (MS-InfoNCE) loss to extract the significant features of the target at 4 scales. Secondly, the method learns the spatial semantic features of the target through a dynamic conditional probability matrix. Finally, this paper designs different feature fusion mechanisms for different scale targets, dynamically fusing multiscale significant features and spatial semantic features to recognize underwater weak targets. The experimental results show that the recognition accuracy of the proposed algorithm is 1.38% higher than that of the existing algorithm when recognizing underwater distorted targets. [ABSTRACT FROM AUTHOR]

In this paper the generalization of the converse of Baer's theorem for two-nilpotent variety of class row (n,m) is carried out. Baer proved that finiteness of G/Zn(G) implies that γn+1(G) is finite. Hekster proved the converse of the Baer's theorem with the assumption that G can be finitely generated. The Baer's theorem can be considered as a result of a classical theorem by Schur denoting that finiteness of G/Z(G) leads to the finiteness of G ′. The converse of the Baer's theorem has been proved conditionally by Taghavi et al. (2019), as well. In the Main Theorem, we prove that, if γm,n(G) ∩ Zn,m(G) = 1 and γm,n+i(G) is finite for some n, i,m ≥ 0. Then G/Zn,m(G) is finite in which γm,n(G) and Zn,m(G) denote verbal and marginal subgroups with respect to two-nilpotent variety of class row (n,m). Thus the generalization of the converse of Baer's theorem for two-nilpotent variety of groups valids by considering i = 0. In this article some other results are attained by the converse of the Baer's theorem. It is also concluded that when n = m = 1. Similar results are obtained for variety of the soluble groups. In addition, the converse of the Schur's theorem which proved by Halasi and Podoski is concluded in this paper, for two-nilpotent variety. We have also obtained some similar results of Chakaneh et al. (2019) for (n,m)-isoclinic family of groups and (1,m)-stem groups. [ABSTRACT FROM AUTHOR]

The precast concrete production process faces uncertainties and risks that reduce the efficiency of the Saudi precast concrete industry. Assessing the risk factors' interdependence yields better results than considering individual analyses only. The previous precast risk studies did not consider the interdependencies among risk factors concerning different process stages. This paper aims to identify precast risk factors and prioritize their importance in Saudi Arabia. Using a dynamic Bayesian network, the assessment considers the interrelationships among the risk factors and different production stages. The risk factors were collected from previous studies, evaluated, and classified into the five construction stages by performing structured interviews with ten experts. The probabilities of the root risks and conditional probabilities of intermediate and leaf risks were assessed based on the ten experts. Then, a Monte Carlo simulation was utilized to provide the status of these probabilities, which were considered input data for the GeNIe program. The main results revealed that erection productivity is the highest risk, with a probability value of 0.87. In addition, the design schedule's significance directly impacts production capacity, resulting in a probability value of 0.74. However, the site management risk is low, with a 0.32 probability value. This paper assists practitioners in optimizing construction schedules. [ABSTRACT FROM AUTHOR]

Using a rich new data source, we explore the selection of economics lecturers into the last UK Research Excellence Framework (REF) exercise. Only some one‐in‐two (54%) of these lecturers were submitted to REF2014; 57% of men and 46% of women. The decision making of institutions is found to be well approximated by a simplified selection approach; focusing on working papers and higher quality journal publications. Our results also reveal sizeable conditional differences in the probability of selection, especially so in departments with higher research rankings. More than half of the variance in selection probability remains unexplained, revealing considerable idiosyncrasies in the management of submissions and uncertainty across the discipline in this research assessment process. [ABSTRACT FROM AUTHOR]

Existing NL2SQL approaches do not fully utilize the information of data table columns, which plays an important role in the semantic understanding of the problem and the logical generation of SQL statements. This paper proposed an NL2SQL method that fused data table columns (FC-SQL) to improve the overall accuracy of SQL generation. Firstly, this method utilized BERT to merge the problem and database table columns for encoded representations. Secondly, it used multitask learning approach to construct a multi-task network by combining parallel and cascade to predict different sub-tasks. Finally, for the conditional value extraction sub-task, this method computed the similarity between the words in the problem and the table columns by fusing the information of the columns, and it used the similarity value as a weight to compute each word as the conditional value probability of each word as a conditional value, thus improving the accuracy of conditional value prediction. The logical form accuracy and SQL execution accuracy on the TableQA dataset reach 88.23% and 91.65%, respectively. This paper designed ablation experiments to verify the effect of table columns information on the model. The experimental results show that the incorporation of table columns improves the effectiveness of the conditional value extraction sub-task, which in turn improves the overall accuracy of the NL2SQL task and provides better SQL generation compared to the comparison model. [ABSTRACT FROM AUTHOR]

Fault localization (FL) is the key activity while debugging a program. Any improvement to this activity leads to significant improvement in total software development cost. In the paper, we present a conditional probability statistics based fault localization technique that derives the association between statement coverage information and test case execution result. This association with the failed test case result shows the fault containing probability of that specific statement. Subsequently, we use a grouping method to refine the obtained statement ranking sequence for better fault localization. We named our proposed FL technique as CGFL, it is an abbreviation of Conditional probability and Grouping based Fault Localization. We evaluated the effectiveness of the proposed method over eleven open-source data sets from Defects4j and SIR repositories. Our obtained results show that on average, the proposed CGFL method is 24.56% more effective than contemporary FL techniques namely D ∗ , Tarantula, Ochiai, Crosstab, BPNN, RBFNN, DNN, and CNN. [ABSTRACT FROM AUTHOR]

This paper studies implications of the consistency conditions among prior, posteriors, and information sets on introspective properties of qualitative belief induced from information sets. The main result reformulates the consistency conditions as: (i) the information sets, without any assumption, almost surely form a partition; and (ii) the posterior at a state is equal to the Bayes conditional probability given the corresponding information set. The main implication of this result is to provide a tractable epistemic model which dispenses with the technical assumptions inherent in the standard epistemic model such as the countable number of information sets. Applications are agreement theorem, no-trade theorem, and the epistemic characterization of correlated equilibria. Implications are as follows. First, since qualitative belief reduces to fully introspective knowledge in the standard environment, a care must be taken when one studies non-veridical belief or non-introspective knowledge. Second, an information partition compatible with the consistency conditions is uniquely determined by the posteriors. Third, qualitative and probability-one beliefs satisfy truth axiom almost surely. The paper also sheds light on how the additivity of the posteriors yields negative introspective properties of beliefs. [ABSTRACT FROM AUTHOR]

In the literature, modifying the baseline distributions and proposing novel approach of the existing probability models play an important role in the analysis of real-world phenomena as well as the development of new distributions essentially stems from the need to adequately characterize environmental and lifetime events. In this paper, we introduce a new model by applying maximum compound technique to the baseline distributions for modeling more types data used in different case of studies including symmetric, asymmetric, skewed and complex data sets. The proposed model called univariate maximum Harris extended Rayleigh distribution and three parameters characterize it, and it is regarded as strong competitor for widely applied symmetrical and non symmetrical models. The paper offers varied fundamental distributional and mathematical properties of the suggested distribution, such as conditional probability and its expectation, as well as quantile and moment-generating functions. The model's parameters are estimated by applying numerous procedures including maximum likelihood estimation, Expectation–Maximization iterative method and Bayesian approach. Bayesian estimation is performed via various loss functions such as square error, Linex and general entropy functions. As well as, approximate and Bootstrap methods are used to construct the confidence interval for the unknown parameters of the proposed model. Additionally, we illustrated different extensive simulation experiments to see the potential of the suggested estimation methods which give satisfactory performance results. In the end, we applied two real data sets for illustrative purposes. In our illustration, we have compared the practicality of the recommended model with several well-known competing distributions. [ABSTRACT FROM AUTHOR]

In many machine learning applications, there are many scenarios when performance is not satisfactory by single classifiers. In this case, an ensemble classification is constructed using several weak base learners to achieve satisfactory performance. Unluckily, the construction of the ensemble classification is empirical, i.e., to try an ensemble classification and if performance is not satisfactory then discard it. In this paper, a challenging analytical problem of the estimation of ensemble classification using the prediction performance of the base learners is considered. The proposed formulation is aimed at estimating the performance of ensemble classification without physically developing it, and it is derived from the perspective of probability theory by manipulating the decision probabilities of the base learners. For this purpose, the output of a base learner (which is either true positive, true negative, false positive, or false negative) is considered as a random variable. Then, the effects of logical disjunction-based and majority voting-based decision combination strategies are analyzed from the perspective of conditional joint probability. To evaluate the forecasted performance of ensemble classifier by the proposed methodology, publicly available standard datasets have been employed. The results show the effectiveness of the derived formulations to estimate the performance of ensemble classification. In addition to this, the theoretical and experimental results show that the logical disjunction-based decision outperforms majority voting in imbalanced datasets and cost-sensitive scenarios. [ABSTRACT FROM AUTHOR]

Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement in concealment. In this paper, we propose a topic-controlled steganography method which is guided by graph-to-text generation. The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs, and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation. Different from traditional linguistic steganography methods, we encode the secret information during graph path coding rather than using conditional probability. We test our method in different aspects and compare it with other text generative steganographic methods. The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to identify relationships between web data and social/economic variables, such as inflation. Our method enables the selection of relevant time series from a large data sample by employing a criterion based on a few hypotheses regarding their dynamics. Specifically, we examine the correlation between web activities and the dynamics of two macroeconomic variables: the unemployment rate and US automotive sales. We demonstrate how changes in the search volume of specific keywords, as measured by corresponding Google Trends data, are reflected in the underlying dynamics of these variables. The findings presented in this paper, along with the versatility of our approach, suggest the potential extension of this study to other economic variables. [ABSTRACT FROM AUTHOR]

Let X1, X2, . . ., Xn be a sequence of coherent random variables, i.e., satisfying the equalities Xj = P(A|Gj), j = 1, 2, . . ., n, almost surely for some event A. This paper contains the proof of the estimate P (max 1=i

Statistical Process Control (SPC) is a technical tool that is used to control and to improve almost any kind of process. However, because of cost consideration, management need to decide which process should apply SPC. In this paper, we propose the use of probability and fuzzy membership function to determine SPC allocation. Conditional probability is used to analyse process failure rate and process repair rate. Then, using Markov Matrix, we calculate the probability of out-of-control process (PO). Nevertheless, in a production line that consists of many parts, the probability value is not adequate to be used as a reference to determine SPC allocation. There are cases for instance, where the value of PO in one part does not mean the same as in other parts since each part may have different sensitivity degree to the final product. For example 0.25 of PO in part 1 may have higher influence to the final product compare to 0.25 of PO in part 2 or part 3. Furthermore, we cannot randomly choose one of those parts to apply SPC or even decide to apply SPC in all parts of the production line. To overcome this problem we propose fuzzy membership function that uses linguistic terms and degree of memberships to analyse PO instead of the probability values. By this mean, the SPC allocation could be determined without ambiguity. For this purpose, the membership function is classified into three categories, namely LOW, MEDIUM and HIGH. Any part with PO fall into the “HIGH” category and high degree of membership is prioritized to apply SPC. [ABSTRACT FROM AUTHOR]

Remote sensing image fusion is dedicated to obtain a high-resolution multispectral (HRMS) image without spatial or spectral distortion compared to the single source image. In this paper, a novel fusion algorithm based on Bayesian estimation for remote sensing images is proposed from the new perspective of risk decisions. In this study, an observation model based on Bayesian estimation for remote sensing image fusion is constructed. Three categories of probabilities including prior, conditional and posterior probabilities are calculated after an intensity-hue-saturation (IHS) transformation is applied to the original low-resolution MS image. To obtain the desired HRMS image, with the corrected posterior probability, a fusion rule based on Bayesian decisions is designed to estimate which pixels to select from the panchromatic (PAN) image and the intensity component of the MS image. The selected pixels constitute a new component that will participate in an IHS inverse transformation to yield the fused image. Extensive experiments were performed on the Pleiades, WorldView-3, and IKONOS datasets, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The task of modeling and analyzing intercepted multifunction radars (MFRs) pulse trains is vital for cognitive electronic reconnaissance. Existing methodologies predominantly rely on prior information or heavily constrained models, posing challenges for non-cooperative applications. This paper introduces a novel approach to model MFRs using a Bayesian network, where the conditional probability density function is approximated by an autoregressive kernel mixture network (ARKMN). Utilizing the estimated probability density function, a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains. Simulation results affirm the proposed method's efficacy in modeling MFRs, outperforming the state-of-the-art in pulse train denoising and change point detection. [ABSTRACT FROM AUTHOR]

Due to the prevalence of the order production mode, multi-variety, small-batch manufacturing enterprises frequently delay deliveries to downstream customers. To date, most existing studies on delayed delivery risk have focused on the response to the risk after it occurs, ignoring how the risk arises. For multi-variety, low-volume production companies, any part of the production process could lead to the ultimate risk of delayed delivery, and the risk is transmissible. Therefore, the path of risk transmission needs to be identified to effectively control the risk of late delivery at key production stages. In this paper, from the perspective of risk transmission, a recognition method based on association rules and the Bayesian network was proposed to identify the risk conduction path. This method firstly determined the strong association rules among the risk factors based on historical data stored in the ERP system and determined the Bayesian network topology structures of the risk transmission path by combining the business process and expert experience. Secondly, the prior and conditional probabilities of each node were determined using data statistics, and the risk transmission path of delayed delivery was identified using the forward and backward reasoning of the Bayesian network. Finally, this paper provided a case study to verify the method, and the following conclusions were obtained: (1) the delay in delivery to downstream customers is mainly due to the delayed delivery of upstream suppliers and the sudden change in customer demand, and (2) the adjustment of enterprise production plans is the key node of the delayed delivery risk transmission path. Through the research in this paper, production companies can identify the target of risk management more scientifically and mitigate the risk through the adjustment of key links. [ABSTRACT FROM AUTHOR]

This document is a response to comments made on a paper about the imputation of counterfactual outcomes. The authors address alternative interpretations of their proposed method and discuss its usefulness in different contexts. They also discuss the relationship between their method and other existing models. The authors emphasize the importance of correctly specifying the model and conditioning information used in estimation and imputation, as well as the implications of misspecification. The document also discusses the pup correction method for improving prediction in statistical models, the challenges of using more complex models, and the importance of considering bias and shift in quantiles when making predictions. The authors suggest an inference procedure that can yield misspecification robust intervals and discuss the construction of prediction intervals for a graph under Gaussianity. They conclude by discussing the use of the BF interval and the pup prediction interval in forecasting exercises, as well as the limitations of the Diebold-Mariano test in the treatment effect setting. The authors report no competing interests and acknowledge the input of others in the development of the paper. [Extracted from the article]

Promoting the greening of industry is the key to achieving high-quality and sustainable development of the urban economy. It is particularly important for resource-based cities (RBCs) that exploit natural resources as the leading industries. In this paper, the Windows-Bootstrap-DEA model was used to calculate the industrial eco-efficiency (IEE) of 114 RBCs in China from 2003 to 2016, and the regional differences and dynamic evolution characteristics of the IEE were analyzed. The panel Tobit model was used to explore the factors associated with IEE in RBCs. The results showed that the IEE of RBCs in China was at a low level during the study period, and the resource utilization process had not reached an optimal state. There were large regional differences in IEE, and there was a significant degree of spatial agglomeration. The results of conditional probability density estimation showed that the distribution of IEE had strong internal stability on the whole, and the distributions of IEE of RBCs in different regions, different resource types, and different development stages showed significant differences. The results of the panel Tobit model showed that per capita GDP, ownership structure, science and technology input, and industrial agglomeration had significant positive effects on IEE, while industrial structure and employment structure showed significant negative effects. The conclusions of this paper can provide a scientific decision-making basis for industrial transformation planning of RBCs. [ABSTRACT FROM AUTHOR]

Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information of the label space. However, the label correlation matrix is usually directly added to the model, which ignores the interactive causality of the correlation between the labels. Considering the label-specific features based on the distance method merely may have the problem of distance measurement failure in the high-dimensional space, while based on the sparse weight matrix method may cause the problem that parameter is dependent on manual selection. Eventually, this leads to poor classifier performance. In addition, it is considered that logical labels cannot describe the importance of different labels and cannot fully express semantic information. Based on these, we propose an Interactive Causal Correlation Space Reshape for Multi-Label Classification (CCSRMC) algorithm. Firstly, the algorithm constructs the label propagation matrix using characteristic that similar instances can be linearly represented by each other. Secondly, label co-occurrence matrix is constructed by combining the conditional probability test method, which is based on the label propagation reshaping the label space to rich label semantics. Then the label co-occurrence matrix combines with the label correlation matrix to construct the label interactive causal correlation matrix to perform multi-label classification learning on the obtained numerical label matrix. Finally, the algorithm in this paper is compared with multiple advanced algorithms on multiple benchmark multi-label datasets. The results show that considering the interactive causal label correlation can reduce the redundant information in the model and improve the performance of the multi-label classifier. [ABSTRACT FROM AUTHOR]

This paper examines the multi-agency identification of risk and harm in extra-familial child exploitation (CE). It explores several data prediction methods to effectively target and prevent harm. It provides a taxonomy analysis of repeat victimisation and cumulative victim harm. It also examines the relationship between age and harm and, finally, the conditional probability of repeat victimisation in exploited children. Research Question Are the most harmed exploited children referred to Multi-Agency Child Exploitation (MACE) Panels, and what other methods exist to identify and prevent high harm in child exploitation? Methods This is a descriptive quantitative statistical analysis using a whole population of children in a Northern English county, aged between 10 and 17 who were recorded in police data as either victims, offenders or MACE referrals between January 2017 and June 2018, encompassing 12,457 children. It utilises an 18-month study window, an 18-month follow-up and 18-month prior time censored period using data between 2015 and 2019 inclusive. This data identifies CE victims using CE flagging and additionally, offence classification with familial abuse and familial exploitation cases removed. It identifies repeat victims and those children referred to MACE for tailored multi-agency intervention. Application of Sherman et al.'s (Policing (Oxford) 10:171–183, 2016) Cambridge Crime Harm Index (CCHI) provided an analysis of harm in victimisation and offending. Findings Exploited repeat victims (90.7%) were not referred to MACE, and there was no significant difference in the harm they sustained in the 18 months following a repeat victimisation compared to exploited children subject to MACE. The most harmed CE victim (Victim-CCHI 15,330) in the 18-month study window was not referred to MACE, nor was the highest frequency CE victim within 18 months (31 victimisations). Exploited victims, victim offenders and MACE children are re-victimised at a significantly higher rate than other children. Exploited victims (73.4%) will not suffer a repeat victimisation of any kind. Forty-two percent of exploited repeat victims will have a third victimisation given a second, and this will attract additional mean victim harm of 464 (for comparison, penetrative sexual activity with a girl under 16 by an Offender 18 or over has a CCHI harm score of 365). Conclusion Whilst MACE provides a forum to share multi-agency information, it only does so for 9.3% of exploited repeat victims. This has implications for the role and focus of MACE. Whilst several quantitative methods were explored to predict harm in CE, this research favours the use of conditional probability and harm association. By using this method, 90.7% of missed repeat victims become visible to professionals. This is essential in providing the opportunity to minimise the risk of further victimisation and increased harm that 42% of this group will have within an 18-month period. This research provides a predictive and evidence-based framework to identify exploited children at risk of further harm and victimisation. [ABSTRACT FROM AUTHOR]

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment. [ABSTRACT FROM AUTHOR]

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the USA.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

In this paper, we study properties of a series–parallel system. The component lifetimes may be dependent and non-identically distributed (DNID) discrete random variables. We consider the number of failed components upon system failure. We derive the probability mass function and the expected value of this quantity. In addition, we find the conditional probabilities corresponding to this variate given some partial information about the system failure. We also provide a numerical example to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

Introduction: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging. Objectives: To predict the +15‐year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists. Material and methods: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment. Results: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging. Conclusion: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images. [ABSTRACT FROM AUTHOR]

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python. [ABSTRACT FROM AUTHOR]

Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. The traditional approach to this challenge is introducing domain knowledge/expert judgments that are encoded as qualitative parameter constraints. In this paper, we focus on multiplicative synergistic constraints. The negative multiplicative synergy constraint and positive multiplicative synergy constraint in this paper are symmetric. In order to integrate multiplicative synergistic constraints into the learning process of Bayesian Network parameters, we propose four methods to deal with the multiplicative synergistic constraints based on the idea of classical isotonic regression algorithm. The four methods are simulated by using the lawn moist model and Asia network, and we compared them with the maximum likelihood estimation (MLE) algorithm. Simulation results show that the proposed methods are superior to the MLE algorithm in the accuracy of parameter learning, which can improve the results of the MLE algorithm to obtain more accurate estimators of the parameters. The proposed methods can reduce the dependence of parameter learning on expert experiences. Combining these constraint methods with Bayesian estimation can improve the accuracy of parameter learning under small sample conditions. [ABSTRACT FROM AUTHOR]

The vocabulary of a language is the collection of words used in that language. The vocabulary learning of English language plays an important role in learning English language. The expansion of learners vocabulary is linked to both their own efforts and the instruments used to guide learning. Better English vocabulary learning software can increase learner's passion for studying as well as their learning efficiency. Therefore, this paper aims to explore an adaptive learning system for English vocabulary based on machine learning. At the beginning, it explains the primary modules of the English vocabulary adaptive learning system, as well as the system's general design and database analysis. Afterwards, the paper looks up the AdaBoost algorithm, where the key parameter is conditional probability, which is primarily used to measure learners' cognitive adaptation to English learning content. The fitness will be updated once learners finish the selection of English vocabulary learning content, which is known as training. The fitness will be changed gradually through training to push the relevant English vocabulary learning material and complete the adaptive learning system for English vocabulary based on machine learning. The experiments result show that the proposed learning system outperforms previous approaches in terms of learning efficiency, scientifically, and reliability. [ABSTRACT FROM AUTHOR]

The capacity for effective communication and sharing of ideas has been instrumental in driving the human evolution. The focus of this paper is on exploring the fundamental mathematical framework that underlies natural language and demonstrating that practical applications can readily uncover theoretical principles like conditional probabilities and informational entropy. This study utilizes a comprehensive Romanian language corpus to conduct a statistical analysis of individual and conditional word probabilities. Additionally, an experimental survey was designed to evaluate the respondents' subconscious selection of follower words. By comparing the results of the survey with the reference results obtained from the statistical analysis of the representative corpus, significant correlations were discovered between the conditional entropy and the survey participants' chosen options. The study considers demographic variables such as age, gender, and education level and evaluates their contribution to the results. The paper also proposes several possible directions for future research. [ABSTRACT FROM AUTHOR]

Predicting the spatial distribution of braided fluvial facies reservoirs is of paramount significance for oil and gas exploration and development. Given that seismic materials enjoy an advantage in dense spatial sampling, many methods have been proposed to predict the reservoir distribution based on different seismic attributes. Nevertheless, different seismic attributes have different sensitivities to the reservoirs, and informational redundancy between them makes it difficult to combine them effectively. Regarding reservoir modeling, multi-point geostatistics represents the distribution characteristics of the braided fluvial facies reservoirs effectively. Despite this, it is very difficult to build high-quality training images. Hence, this paper proposes a three-step method of predicting braided fluvial facies reservoirs based on probability fusion and multi-point geostatistics. Firstly, similar statistical data of modern sedimentation and field paleo-outcrops were processed under the guidance of the sedimentation pattern to construct reservoir training images suitable for the target stratum in the research area. Secondly, each linear combination of selected seismic attributes was demarcated to calculate the principal component value and work out the elementary conditional probability. Lastly, the PR probability integration approach was employed to combine all conditional probabilities and calculate the joint probability. Then the joint probability was combined with training images to build a reservoir distribution model through multi-point geostatistics. We illustrated the detailed workflow of our new method by applying it to a braided fluvial reservoir modeling case in the Bohai Bay Basin, East China. The new method reduced the error of prediction results by 32% and 46% respectively, and the error of water content by 36.5% and 60.3%. This method is a potentially effective technique to predict and characterize the reservoir spatial distribution and modeling in other oil fields with the same geological background. [ABSTRACT FROM AUTHOR]

Collisions and groundings account for more than 80% among all types of maritime accidents, and risk assessment is an essential step in the formal safety assessment. This paper proposes a method based on fuzzy fault tree analysis and Noisy-OR gate Bayesian network for navigational risk assessment. First, a fault tree model was established with historical data, and the probability of basic events is calculated using fuzzy sets. Then, the Noisy-OR gate is utilized to determine the conditional probability of related nodes and obtain the probability distribution of the consequences in the Bayesian network. Finally, this proposed method is applied to Qinzhou Port. From sensitivity analysis, several predominant influencing factors are identified, including navigational area, ship type and time of the day. The results indicate that the consequence is sensitive to the position where the accidents occurred. Consequently, this paper provides a practical and reasonable method for risk assessment for navigational accidents. [ABSTRACT FROM AUTHOR]

In this paper we introduce probabilistic measure which takes values in bihyperbolic numbers and generalize Kolmogorov's system of axioms. We study the zero divisors of the set of bihyperbolic numbers and keeping it in mind, we define conditional bihyperbolic probability and prove bihyperbolic analogues of the multiplication theorem, of the law of total probability and Bayes' theorem. [ABSTRACT FROM AUTHOR]

To promote the development of intelligent operation and maintenance of hydropower units, this paper proposes a robust multi-order statistic pooling (SAMOSP) normalized flow conditional probability model(NFCPM) for unsupervised abnormal sound detection of hydropower units. A self-attentive multi-order statistic pooling module (SAMOSP) is proposed for the first time in the paper. The module starts with a one-dimensional compressed convolutional layer and a bottleneck compressing the weight vector of the excitation part of the self-attentive to time frame. The weight vectors are used to compute the multi-order statistical pooling vectors. Then the different weights of the self-attentive to multi-order statistical pooling quantities of the frequency bands are subdivided and information about the significant statistics of the different frequency bands is extracted by them to obtain the self-attentive statistical pooling feature vector of the time-frequency map. Test samples of different time periods are tested in this normal data probability model, and the corresponding scores are obtained. The lower score indicates the lower probability density, the greater the degree of abnormality, thus realizing the unsupervised abnormality detection of the audio signal of the hydropower units. [ABSTRACT FROM AUTHOR]

Earthen dams are exposed to complex environments where their safety is often affected by multiple uncertain risks. A Bayesian network (BN) is often used to analyze the dam failure risk, which is an effective tool for this issue as its excellent ability in representing uncertainty and reasoning. The validity of the BN model is strongly dependent on the quality of the sample data, making convincing modeling rationale a challenge. There has been a lack of systematic analysis of the dam failure data of China, resulting in limited exploration of the potential associations between risk factors. In this paper, we established a comprehensive database containing various dam failure cases in China. Herein, historical dam failure statistics are used to develop BN models for risk analysis of earthen dams in China. In order to unleash the value of the historical data, we established a Bayesian network through the Causal Loop Diagrams (CLD) based on the nonlinear causal analysis. We determined the conditional probabilities using Word Frequency Analysis (WFA). By comparing with the Bayesian Learning results, the modeling method of BN proposed in our study has apparent advantages. According to the BN model established in this paper, the probabilities of dam failure due to seepage damage, overtopping, and structural instability are estimated to be 22.1%, 58.1%, and 7.9%, respectively. In addition, we presented a demonstration of the inference process for the dam failure path, which will offer valuable insights to dam safety practitioners during their decision-making process. [ABSTRACT FROM AUTHOR]

Commonly, it is accepted that oncology treatment would yield outcomes with a certain determinism without any quantitative support or mathematical model that establishes such determinations. Nowadays, with the advent of nanomedicine, the targeting drug delivery scheme has emerged, whose central objective is the uptake of nanoparticles by tumors. Once they are injected into the bloodstream, it is unclear as to which process governs the directing of nanoparticles towards the desired target, deterministic or stochastic. In any scenario, an optimal outcome, small toxicity and minimal dispersion of drugs is expected. Commonly, it is expected that an important fraction of them can be internalized into tumor. In this manner, due to the fraction of nanoparticles that have failed to uptake, the success of the drug delivery scheme might be at risk. In this paper, a theory based on the sequence electrodynamics–diffusion–Bayes theorem is presented. The Bayesian probability that emerges at the end of the sequence might be telling us that dynamical processes based on the injection of electrically charged nanoparticles might be dictated by stochastic formalism. Thus, rather than expecting a deterministic process, the chain of events would convert the drug delivery scheme to be dependent on a sequence of conditional probabilities. [ABSTRACT FROM AUTHOR]

Background: Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental studies. However, due to the nature of experimental studies, financial resources, human resources, and patients' ethical considerations, researchers cannot fully control the exposure of the research participants. Furthermore, most existing observational research designs are limited to determining causal relationships and cannot handle observational data, let alone determine the dosages needed for medical research. Results: This paper presents a new experimental strategy called quasi-intervention for quantifying the causal effect between disease and treatment options in observed data by using a causal inference method, which converts the potential effect of different treatment options on disease into computing differences in the conditional probability. We evaluated the accuracy of the quasi-intervention by quantifying the impact of adjusting Chinese patients' neutrophil-to-lymphocyte ratio (NLR) on their overall survival (OS) (169 lung cancer patients and 79 controls).The results agree with the literature in this study, consisting of nine papers on cohort studies on the NLR and the prognosis of lung cancer patients, proving that our method is correct. Conclusion: Taken together, the results imply that quasi-intervention is a promising method for quantifying the causal effect between disease and treatment options without clinical trials, and it could improve confidence about treatment options' efficacy and safety. [ABSTRACT FROM AUTHOR]

System models based on Bayesian networks (BNs) are widely applied in different areas. This paper facilitates the use of such models by advancing the ranked nodes method (RNM) for constructing conditional probability tables (CPTs) of BNs by expert elicitation. In RNM, the CPT of a child node is generated using a function known as the weight expression and weights of parent nodes that are elicited from the expert. However, there is a lack of exact guidelines for eliciting these parameters which complicates the use of RNM. To mitigate this issue, this paper introduces a novel framework for supporting the RNM parameter elicitation. First, the expert assesses the two most probable states of the child node in scenarios that correspond to extreme states of the parent nodes. Then, a feasible weight expression and a feasible weight set are computationally determined. Finally, the expert selects weight values from this set. [ABSTRACT FROM AUTHOR]

Crane scheduling tasks in steelworks are a matter of uncertainty scheduling with certain probability distribution pattern. To better schedule tasks with this feature, this paper proposes a Dynamic Area Allocation (DAA)-based crane scheduling model according to the following steps. First, Bayesian network, according to the time sequence of crane transportation tasks, is constructed. Then, conditional probability for each network node on the basis of actual crane operating data is calculated for getting the corresponding time–space probability distribution, and then obtaining the spatial distribution by superposing of all crane transportation tasks in the space domain at certain time. At last, tasks are assigned to cranes based on their spatial distribution and the equal probability partition. Simulation testing on the scheduling model is carried out using practical crane transportation tasks in steelworks. Results show that the model based on the dynamic area allocation, with its scheduling period of 15 min, can greatly shorten transportation time and reduce times of collision resulted from crane interference, after compared with the current widely used crane scheduling programme based on the fixed area allocation (FAA). [ABSTRACT FROM AUTHOR]

In this paper, we perform estimates of vulnerability to multidimensional poverty for 17 Latin American countries at three points of time: 2005/2006, 2012, and 2017. We use a Multidimensional Bayesian Network Classifier model to estimate the conditional probability of being multidimensionally poor and then we use these probabilities and the standard downside semi‐deviation as the risk parameter to identify the vulnerable households. Despite significant reductions over the study period, in 2017 approximately 150 million people—excluding Guatemala, Nicaragua and Venezuela for which we do not have recent data—remained vulnerable to multidimensional poverty. We also observe that vulnerability to poverty is reduced at a much slower rate than poverty itself, revealing that achievements in SDG1 can be quite fragile. We perform a decomposition and find that as poverty decreases, risk‐induced vulnerability becomes relatively more important than poverty‐induced vulnerability. However, the poor‐vulnerable still constitute the core vulnerability group. [ABSTRACT FROM AUTHOR]

Modelling one‐to‐many type mappings in problems with a temporal component can be challenging. Backpropagation is not applicable to networks that perform discrete sampling and is also susceptible to gradient instabilities, especially when applied to longer sequences. In this paper, we propose two recurrent neural network architectures that leverage stochastic units and mixture models, and are trained with target propagation. We demonstrate that these networks can model complex conditional probability distributions, outperform backpropagation‐trained alternatives, and do not rapidly degrade with increased time horizons. Our main contributions consist of the design and evaluation of the architectures that enable the networks to solve multi‐model problems with a temporal dimension. This also includes the extension of the target propagation through time algorithm to handle stochastic neurons. The use of target propagation provides an additional computational advantage, which enables the network to handle time horizons that are substantially longer compared to networks fitted using backpropagation. [ABSTRACT FROM AUTHOR]

Every year the volume of information is growing at a high rate; therefore, more modern approaches are required to deal with such issues efficiently. Distributed systems, such as Apache Spark, offer such a modern approach, resulting in more and more machine learning models, being adapted into using distributed logic. In this paper, we propose a classification model, based on Bayesian Networks (BNs), that utilizes the distributed environment of Apache Spark using the Dataframes paradigm. This model can exploit any user-provided directed acyclic graph (DAG) that portrays the dependencies between the features of a dataset to estimate the parameters of the conditional probability distributions associated with each node in the graph to make accurate predictions. Moreover, in contrast with the majority of implementations that are only able to handle discrete features, it is also capable of efficiently handling continuous features by calculating the Gaussian probability density function. [ABSTRACT FROM AUTHOR]

We estimate an infinite-horizon dynamic oligopoly model of audit firm tenure and misstatements and evaluate a policy counterfactual involving mandatory audit firm rotation. Longer tenure lowers the cost of producing audits, increasing audit quality and reducing audit fees. Thus clients are less likely to misstate and more likely to keep the incumbent audit firm as tenure increases. By reducing the value of retaining audit firms, mandatory rotation leads to large increases in auditor switches, even before the term limit, implying increases in the switching costs borne by clients. Misstatement rates increase because audit firms endogenously lower audit quality and newly hired audit firms have lower quality. Overall, the model suggests caution when evaluating the costs and benefits of government oversight over the audit profession. This paper was accepted by Suraj Srinivasan, accounting. Funding: The authors thank the financial support from the Olin Busiiness School and the Wharton School of the University of Pennsylvania. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4944. [ABSTRACT FROM AUTHOR]

This paper calls for a re-appraisal of McGee's analysis of the semantics, logic and probabilities of indicative conditionals presented in his 1989 paper Conditional probabilities and compounds of conditionals. The probabilistic measures introduced by McGee are given a new axiomatisation—built on the principle that the antecedent of a conditional is probabilistically independent of the conditional—and a more transparent method of constructing such measures is provided. McGee's Dutch book argument is restructured to more clearly reveal that it introduces a novel contribution to the epistemology of semantic indeterminacy, and shows that its more controversial implications are unavoidable if we want to maintain the Ramsey Test along with the standard laws of probability. Importantly, it is shown that the counterexamples that have been levelled at McGee's analysis—generating a rather wide consensus that it yields 'unintuitive' or 'wrong' probabilities for compounds —fail to strike at their intended target; for to honour the intuitions of the counterexamples one must either give up the Ramsey Test or the standard laws of probability. It will be argued that we need to give up neither if we take the counterexamples as further evidence that the indicative conditional sometimes allows for a non-epistemic 'causal' interpretation alongside its usual epistemic interpretation. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor network (UWSN) is one of the effective methods to acquire ocean observation data, and research on underwater routing technology has become a hot spot. However, the lifetime of UWSN is an important factor affecting the overall performance of routing protocols. Due to the uncertain underwater environment and poor link quality, it will affect data integrity and network survival ability. Cooperative communication is an excellent technique for solving such problems. In this communication, the source node forwards data through a cooperative path to improve network throughput, while the destination node receives data packets that do not contain obvious errors. Therefore, this paper provides a cooperative communication method to solve the packet loss problem and lifetime of UWSN. The underwater-layered routing network structure is adopted to divide the network nodes into clusters for energy balance. Compared with the traditional layered protocol, the cooperative communication can better guarantee the link quality of the underwater channel. The k-means algorithm is also used to cluster nodes, and conditional probability can select cluster heads. During data transmission, relay nodes will amplify the signal and backup the data packets to avoid dropping packets. The proposed protocol is simulated and compared with non-cooperative protocols (LDBR and MLCEE), the results show that the proposed protocol outperformed the other protocols in terms of network lifetime, throughput, energy consumption and end-to-end delay. [ABSTRACT FROM AUTHOR]