Search

Sounds are more easily transmitted underwater than through air and many freshwater fish species can hear them, particularly over the low frequencies. Recent studies on freshwater fish evidenced that hearing sensitivities can be limited by the level of ambient noise, a phenomenon also known as acoustic masking. However, it is still unclear whether variations in ambient noises, such as those produced by human activities, may alter fish trophic interactions.The general objective of this study was to propose and evaluate a theoretical framework explicitly linking fish attack rates to species hearing sensitivities and ambient noise levels in freshwater ecosystems. The proposed model shows that the feeding activity of fish at an intermediate position in the food web is conditional on the probability of being acoustically detected by their predators, or of encountering an acoustically distressed resource.Model simulations and preliminary field results suggest that yellow perch (Perca flavescens) could feed more actively in the presence of augmented ambient noise levels. Yellow perch captures per unit effort were higher by a factor of 2.7 in noisy versus quiet days. We argue that fish exposed to intermediate levels of noise could maximise the probability of detecting food patches while minimising their predation risk.Acoustic monitoring programmes for freshwater ecosystems require a fundamental knowledge of underwater noise levels, species hearing sensitivities and features affecting sound propagation. The approach proposed in this paper is seminal in linking the above descriptors in a coherent mathematical framework to understand the effect of underwater sounds on trophic interactions. Such framework is needed to make testable predictions and generalise to other taxa and ecological contexts. [ABSTRACT FROM AUTHOR]

This work introduces the English Sublexical Toolkit, a suite of tools that utilizes an experience-dependent learning framework of sublexical knowledge to extract regularities from the English lexicon. The Toolkit quantifies the empirical regularity of sublexical units in both the reading and spelling directions (i.e., grapheme-to-phoneme and phoneme-to-grapheme) and at multiple grain sizes (i.e., phoneme/grapheme and onset/rime unit size). It can extract multiple experience-dependent regularity indices for words or pseudowords, including both frequency indices (e.g., grapheme frequency) and conditional probability indices (e.g., grapheme-to-phoneme probability). These tools provide (1) superior estimates of the regularities that better reflect the complexity of the sublexical system relative to previously published indices and (2) completely novel indices of sublexical units such as phonographeme frequency (i.e., combined units of individual phonemes and graphemes that are independent of processing direction). We demonstrate that measures from the toolkit explain significant amounts of variance in empirical data (naming of real words and lexical decision), and either outperform or are comparable to the best available consistency measures. The flexibility of the toolkit is further demonstrated by its ability to readily index the probability of different pseudowords pronunciations, and we report that the measures account for the majority of variance in these empirically observed probabilities. Overall, this work provides a framework and resources that can be flexibly used to identify optimal corpus-based consistency measures that help explain reading/spelling behaviors for real and pseudowords. [ABSTRACT FROM AUTHOR]

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts. [ABSTRACT FROM AUTHOR]

This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the reconstructability of quantum mechanics from classical conditional probabilities representing measurement outcomes conditioned on measurement choices. We will investigate how the quantum mechanical representation of classical conditional probabilities is situated within the broader frame of noncommutative representations. To this goal, we adopt some parts of the quantum formalism and ask whether empirical data can constrain the rest of the representation to conform to quantum mechanics. We will show that as the set of empirical data grows, conventional elements in the representation gradually shrink and the noncommutative representations narrow down to the unique quantum mechanical representation. [ABSTRACT FROM AUTHOR]

Bayesian networks are now widespread for modelling uncertain knowledge. They graph probabilistic relationships, which are quantified using conditional probability tables (CPTs). When empirical data are unavailable, experts may specify CPTs. Here we propose novel methodology for quantifying CPTs: a Bayesian statistical approach to both elicitation and encoding of expert-specified probabilities, in a way that acknowledges their uncertainty. We illustrate this new approach using a case study describing habitat most at risk from feral pigs. For complicated CPTs, it is difficult to elicit all scenarios (CPT entries). Like the CPT Calculator software program, we ask about a few scenarios (e.g. under a one-factor-at-a-time design) to reduce the experts' workload. Unlike CPT Calculator, we adopt a global rather than local regression to 'fill out' CPT entries. Unlike other methods for scenario-based elicitation for regression, we capture uncertainty about each probability in a sequence that explicitly controls biases and enhances interpretation. Furthermore, to utilize all elicited information, we introduce Bayesian rather than Classical generalised linear modelling (GLM). For large CPTs (e.g. >3 levels per parent) we show Bayesian GLM supports richer inference, particularly on interactions, even with few scenarios, providing more information regarding accuracy of encoding. [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]

Environmental flow (e-flow) decision making relies on flow-ecology models to predict ecological outcomes under different flow regimes. While expert knowledge has traditionally informed these models, there is increasing use of data-driven approaches. We investigated data integration for Bayesian conditional probability networks (CPNs) developed through expert elicitation in an e-flows assessment in Victoria, Australia. Using synthetic datasets based on monitoring data, we assessed the impact of varying data characteristics on model outcomes. Incorporating 10 years of data had the greatest influence on model predictions compared to the expert-based models, with diminishing additional effect for longer records. Notably, the expert model based on the most non-conforming expert opinion was more sensitive to data integration than a model based on harnessing the opinion of all experts, highlighting the importance of considering diverse experiential knowledge. We provide recommendations on leveraging limited datasets and models to guide efficient monitoring and management. • Exploration of a method to incorporate monitoring data into expert-opinion built Bayesian CPNs. • Synthetic data records of varying characteristics were used to assess impact on model outcomes. • Demonstrate that complementary use of expert opinion and empirical data can enrich modeling approaches. • Monitoring data can complement expert elicitation in models to assess uncertainty and direct management efforts. [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]

• A new dependency assessment method was developed for human reliability analysis. • The method mathematically quantifies the conditional probability of a subsequent event. • The method analyzes six features of dependency between two events. • Quantitative bases for estimating the conditional probability are provided. • Two application studies demonstrate the feasibility of the proposed method. Dependency assessment is an aspect of human reliability analysis that identifies the causal relationship between two human events and quantifies the conditional probability of the successor event when two or more events exist in an accident sequence. Despite broad recognition of the impact of dependency on the overall system risk, many experts have been concerned that most current methods are rooted in the THERP method without a sufficient theoretical and empirical basis for dependency models. In this study, we propose a method that calculates the conditional failure probability of a successor event based on quantitative evidence of the dependency between two human events. Quantitative assessment is performed by evaluating six features and integrating the failure probabilities due to the features into the assessment based on an arithmetic equation. The estimates obtained from this empirical data analysis, a statistical function for time insufficiency, and a sequence alignment algorithm were employed to support the basis of the calculation with several assumptions. Two case studies are presented to show the feasibility of the study and the result differences between the proposed and existing methods. Since this study presents a new approach to dependency assessment, additional issues to be tackled regarding the assumptions and technical bases used are discussed with further research directions. [ABSTRACT FROM AUTHOR]

Understanding, quantifying and predicting market fluctuation has become increasingly important in recent decades. Volatility and volume are the two commonly used quantities to study the market dynamics and the relationship between these two has been modeled and debated for years with several hypothesis been put forward. Using empirical data, we investigate the causality and correlation between volume and volatility and find new ways in which they interact, particularly when the levels of both are high. We find that the volume-conditional volatility distribution scales with volume as a power-law function with an exponential cutoff. We exploit the characteristics of a volume-volatility scatterplot and find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), the highest volatility observed in a given range of volume. This supports our empirical analysis, showing that volume is an effective parameter for prediction of the maximum value of volatility for both same-day and near-future time periods. The joint conditional probability of volume and volatility also indicates if we invoke both quantities, the prediction of the largest next-day volatility will be better than invoking either one alone. This approach is thus a greatly improved method of risk assessment. • Multiscale volume-conditional volatility distributions are unified. • Volume-conditional volatility distributions are well described by power-laws with exponential cutoffs. • The highest volatility in a given range of volume is strongly correlated with previous-day volume. • Both volume and volatility have the predicting power for the volatility. [ABSTRACT FROM AUTHOR]

Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the stability of oscillators, such as clocks and lasers, ranging from short to long time scales. Although these two statistical measures were developed independently for different purposes in different fields, their interest lies in examining the multiscale temporal structures of physical phenomena under study. We demonstrate that from an information-theoretical perspective, they share some foundations and exhibit similar tendencies. We experimentally confirmed that similar properties of the MSE and Allan variance can be observed in low-frequency fluctuations (LFF) in chaotic lasers and physiological heartbeat data. Furthermore, we calculated the condition under which this consistency between the MSE and Allan variance exists, which is related to certain conditional probabilities. Heuristically, natural physical systems including the aforementioned LFF and heartbeat data mostly satisfy this condition, and hence, the MSE and Allan variance demonstrate similar properties. As a counterexample, we demonstrate an artificially constructed random sequence, for which the MSE and Allan variance exhibit different trends. [ABSTRACT FROM AUTHOR]

The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (Rosenbaum & Rubin, 1983), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so the propensity score explicitly does not involve any outcome variables, in contrast to other summaries of variables sometimes used in observational studies. Balancing the distributions of covariates in the treatment and control groups by matching or balancing on the propensity score is therefore an aspect of the design of the observational study. In this invited comment on our 1983 article, we review the situation in the early 1980s and recall some apparent paradoxes that propensity scores helped to resolve. We demonstrate that it is possible to balance an enormous number of low-dimensional summaries of a high-dimensional covariate, even though it is generally impossible to match individuals closely for all the components of a high-dimensional covariate. In a sense, there is only one crucial observed covariate, the propensity score, and there is one crucial unobserved covariate, the principal unobserved covariate. The propensity score and the principal unobserved covariate are equal when treatment assignment is strongly ignorable, that is, unconfounded. Controlling for observed covariates is a prelude to the crucial step from association to causation, the step that addresses potential biases from unmeasured covariates. The design of an observational study also prepares for the step to causation: by selecting comparisons to increase the design sensitivity, by seeking opportunities to detect bias, by seeking mutually supportive evidence affected by different biases, by incorporating quasi-experimental devices such as multiple control groups, and by including the economist's instruments. All of these considerations reflect the formal development of sensitivity analyses that were largely informal prior to the 1980s. [ABSTRACT FROM AUTHOR]

Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight "conditional-probability-based" bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a "classification-free" modification, in which factor classifications are avoided and the result is dominated by a single parameter, "bin width". Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

Conditionals with conditional constituents pose challenges for the Thesis, the idea that the probability of a conditional is the corresponding conditional probability. This note is concerned with two proposals for overcoming those challenges, both inspired by early work of van Fraassen: the Bernoulli Semantics associated with Stalnaker and Jeffrey, and augmented with a mechanism for obtaining "local probabilities" by Kaufmann; and a proposal by Bacon which I dub Ordinal Semantics. Despite differences in mathematical details and emphasis of presentation, both proposals lend themselves for use as a basis for a modal-theoretic interpretation of embedded conditionals. The goal of this note is to compare the two frameworks by implementing a model for the interpretation of conditionals in each, based on the same underlying probability model for non-conditional sentences. I show that in the Ordinal model, certain sentences are assigned probabilities that do not accord with intuitions. This problem is familiar from the literature on Bernoulli models and can be addressed by introducing Kaufmann-style local probabilities into Ordinal models. I then show that Bernoulli Semantics has other limitations, in that it assigns probabilities in violation of the Thesis to certain very complex formulas. The upshot is that a fusion of the theories may be our best shot at getting the predictions right. [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]

The present paper evaluates five methods for building Conditional Probability Tables (CPTs) of Bayesian Belief Networks (BBNs) from partial expert information: functional interpolation, the Elicitation BBN, the Cain calculator, Fenton et al. and Røed et al. methods. The evaluation considers application to a specific field of risk analysis, Human Reliability Analysis (HRA). The five methods are particularly suited for HRA models calculating the human error probability as a function of influencing factor assessments. The performance of the methods is evaluated on two simple examples, designed to test aspects relevant for HRA (but not exclusively): the representation of strong factor influences and interactions, the representation of uncertainty on the BBN relationships, and the method requirements as the BBN size increases. The evaluation underscores modelling limitations related to the treatment of multi-factor interdependencies and of different degrees of uncertainty in the factor relationships. The functional interpolation method is the least susceptible to these limitations; however, its elicitation requirements grow exponentially with the model size. Besides expert judgment, HRA applications of BBNs include the use of empirical data, combination of data and judgment, information from existing HRA methods: the building of the CPTs in these applications is outside the scope of the evaluation. [ABSTRACT FROM AUTHOR]

We propose a multiplicity‐robust estimation method for static or dynamic games. The method allows for distinct behaviors and strategies across markets by treating market‐specific behaviors as correlated latent variables, with their conditional probability measure treated as an infinite‐dimensional nuisance parameter. Instead of solving the intermediate infinite‐dimensional optimization problem, we consider the equivalent finite‐dimensional dual problem. This property allows for a practically feasible characterization of the identified region for the structural parameters. We apply the estimation method to newspaper market previously studied in Gentzkow et al. (American Economic Review 104 (2014), 3073–114) to characterize the identified region of marginal costs. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities. In the case where the quantities of interest are conditional probability distributions, the natural relationship between the quantities can be exploited to impute missing probabilities. Here we test the Bayesian intelligence interpolation method and its variations for Bayesian network conditional probability tables, called "InterBeta." We compare the various outputs of InterBeta on two cases where conditional probability tables were elicited from groups of experts. We show that interpolated values are in good agreement with experts' values and give guidance on how InterBeta could be used to good effect to reduce expert burden in SEJ exercises. [ABSTRACT FROM AUTHOR]

According to the PCCP thesis, the probability of a conditional A → C is the conditional probability P(C|A). This claim is undermined by Lewis' triviality results, which purport to show that apart from trivial cases, PCCP is not true. In the present article we show that the only rational, "Dutch Book-resistant" extension of the agent's beliefs concerning non-conditional sentences A and C to the conditional A → C is by assuming that P(A → C) = P(C|A) (i.e., in accord with PCCP). In other cases a diachronic Dutch Book against the agent can be constructed. There is a tension between our findings and Lewis' results, which needs to be explained. Therefore, we present a probability space which corresponds in a natural way to the diachronic Dutch Book—and which allows the conditional A → C to be interpreted as an event in a mathematically sound way. It also allows to formalize the notion of conditionalizing A → C on ¬C which plays a crucial role in Lewis' proof. Our conclusion is that Lewis' proof is circular, so it cannot be considered to be a sound argument against PCCP. [ABSTRACT FROM AUTHOR]

We suggest a normative model for the evaluation of explanations B because A based on probabilistic conditional reasoning and compare it with empirical data. According to the modus ponens model of explanations, the probability of B because A should equal the joint probability of the conditional if A then B and the explanans A. We argue that B because A expresses the conjunction of A and B as well as positive relevance of A for B. In Study 1, participants (N = 80) judged the subjective probabilities of 20 sets of statements with a focus on belief-based reasoning under uncertainty. In Study 2, participants (N = 376) were assigned to one of six item sets for which we varied the inferential relevance of A for B to explore boundary conditions of our model. We assessed the performance of our model across a range of analyses and report results on the Equation, a fundamental model in research on probabilistic reasoning concerning the evaluation of conditionals. In both studies, results indicate that participants' belief in statements B because A followed model predictions systematically. However, a sizeable proportion of sets of beliefs contained at least one incoherence, indicating deviations from the norms of rationality suggested by our model. In addition, results of Study 2 lend support to the idea that inferential relevance may be relevant for the evaluation of both conditionals and explanations. [ABSTRACT FROM AUTHOR]

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing. [ABSTRACT FROM AUTHOR]

Coseismic surface faulting is a significant source of hazard for critical plants and distributive infrastructure; it may occur either on the principal fault or as distributed rupture on nearby faults. Hazard assessment for distributed faulting is based on empirical relations which, in the case of normal faults, were derived almost 15 years ago using a dataset of US earthquakes. We collected additional case histories worldwide, for a total of 21 earthquakes, and calculated the conditional probability of distributed faulting as a function of distance from the principal fault. We found no clear dependency on the magnitude nor the time of occurrence of the earthquakes, but our data consistently show a higher probability of rupture when compared with the scaling relations currently adopted in engineering practice. We derive updated empirical regressions and show that the results are strongly conditioned by the averaging of earthquakes effectively generating distributed faulting at a given distance and those which did not generate faulting; thus, we introduce a more conservative scenario that can be included in a logic tree approach to consider the full spectrum of potential ruptures. Our results can be applied in the framework of probabilistic assessment of fault displacement hazard. [ABSTRACT FROM AUTHOR]

The use of differential case marking of A and P has been explained in terms of efficiency (economy) and markedness. The present study tests predictions based on these accounts, using conditional probabilities of a particular feature given the syntactic role (cue availability), and conditional probabilities of a particular syntactic role given the feature in question (cue reliability). Cue availability serves as a measure of markedness, whereas cue reliability is central for the efficiency account. Similar to reverse engineering, we determine which of the probabilistic measures could have been responsible for the recurrent cross-linguistic patterns described in the literature. The probabilities are estimated from spontaneous informal dialogues in English and Russian (Indo-European), Lao (Tai-Kadai), N||ng (Tuu) and Ruuli (Bantu). The analyses, which involve a series of mixed-effects Poisson models, clearly demonstrate that cue reliability matches the observed cross-linguistic patterns better than cue availability. Thus, the results support the efficiency account of differential marking. [ABSTRACT FROM AUTHOR]

The 27 November 2019 Mw 6.0 earthquake that occurred in the southwestern part of the Hellenic Arc near Crete Island provided evidence of the high potential for strong earthquakes and active seismicity in the Hellenic Arc. In addition, tsunamis have been reported to occur for the region after major earthquakes in the historical past, so the seismic hazard of the Hellenic Arc should be evaluated in detail. The aim of this study is to evaluate the seismic hazard of the Hellenic Arc more reliably and accurately by estimating the conditional probabilities of a strong earthquake based on Weibull, gamma, log-normal, exponential, Rayleigh, and inverse Gaussian distribution models for the inter-event time of Mw ≥ 6.0 earthquakes that occurred between 1900 and 2019 in the study area. The fit between each model and the data was tested using four different test criteria, namely the log-likelihood value, Akaike information criterion, Bayesian information criteria, and Kolmogorov–Smirnov test. According to the results, the inverse Gaussian distribution was selected as the best, the log-normal distribution as the second best, the Weibull and gamma distributions as intermediate, and the Rayleigh and exponential distribution as the poorest and second poorest model, respectively. The conditional probability of an earthquake with magnitude Mw ≥ 6.0 is estimated to be higher than 0.70 according to all of the models used in this study for the base year te = 0 (te = 2015) and t > 5 years (t > 2020). Moreover, the results obtained based on the inverse Gaussian, exponential, log-normal, and Weibull distribution models are close to each other and are higher than 0.60 for te = 0 and t ≥ 3 years (t ≥ 2018). The outcomes of this study when using the different distribution models will contribute to assessments of the seismic as well as tsunami hazards for the region. [ABSTRACT FROM AUTHOR]

Collapse is one of the main hazards during tunnel construction by the drill-and-blast method. In order to evaluate the collapse risk and provide a basis for risk control, a failure probability evaluation method for collapse of drill-and-blast tunnels based on the multistate fuzzy Bayesian network is proposed in this paper. First, the typical tunnel collapse cases are analyzed statistically based on the risk breakdown structure method, a fault tree model is built for drill-and-blast tunnel collapses and the causal relationships between the tunnel collapse and the influential factors, such as natural conditions, engineering geology and construction etc., are revealed. Secondly, the multiple fault states of nodes, including rock mass grade and groundwater, are described by the fuzzy numbers. The fuzzy subsets are utilized to describe the failure probability of nodes and the uncertain logic relationship between nodes described by the multistate fuzzy conditional probability table is established. In order to ensure the reliability of the survey data when evaluating the possibility intervals of the multistate fuzzy conditional probability tables and the fuzzy failure probability of root nodes, as well as taking the expert judgment ability level and subjective reliability level into consideration, an expert investigation method based on the confidence indicator is proposed. Finally, in order to fully exploit expert knowledge and empirical data, the α-weighted valuation method is adopted for defuzzification so as to obtain precise parameters for the conditional probability tables. The 3σ criterion is employed to calculate the characteristic values of triangular fuzzy numbers so as to determine the prior fuzzy failure probability of root nodes. By means of the fuzzy Bayesian inference, the proposed method is capable of calculating the probability distribution of tunnel collapse and identifying the critical risk factors under both prior knowledge and given evidence circumstances. Taking the collapse failure probability evaluation for the Xiucun Tunnel passing through the fault F18 as an example, the application results demonstrated the feasibility and efficiency of the proposed method and it can be utilized as a decision-making tool for safety risk management during tunnel construction. • The influential factors causing tunnel collapse are identified. • The multiple fault states of factors and uncertain logic relationship between factors are considered. • A multistate fuzzy BN based method is proposed for collapse failure probability analysis of drill-and-blast tunnels. • The proposed method can handle multistate vagueness and uncertainty compared to the traditional BN. [ABSTRACT FROM AUTHOR]

This study attempts to estimate the probability of the occurrence of large earthquakes (Mw ≥ 5.5) in the northwestern part of Haryana state, India, where a new nuclear power plant (NPP) is going to be constructed in the near future. First, an earthquake catalogue is developed for the period 1803–1986, and five stochastic models, namely lognormal, Weibull, gamma, Rayleigh, and double exponential, are then applied to past earthquake data. The performance of these models is checked using three statistical tests, and the lognormal, Weibull, and Rayleigh models are found to produce good approximations for this region, whereas the double exponential and gamma models yield intermediate and poor results. Hence, cumulative and conditional probabilities and related hazard curves for future earthquakes are estimated using the most suitable models. The cumulative probability of the occurrence of an earthquake (Mw ≥ 5.5) since the last event (1986) reached 0.95–0.98 as of 2018. The conditional probability of the occurrence of such an earthquake reaches 0.90–0.95 about 9–12 years from now (2027–2030), when the elapsed time will be 32 years (i.e., since 2018). The probability of earthquakes with different threshold magnitudes is then estimated, and based on the outcome of this investigation, earthquake magnitudes are classified from occasional (Mw ≤ 6.1) to very rare events (Mw ≥ 7.6) that this region may experience in the future. The findings of this study will be considered in seismic hazard assessment, liquefaction hazard assessment, and earthquake-resistant design of NPP components. [ABSTRACT FROM AUTHOR]

This paper examines the complex dependence between peak district heating demand and outdoor temperature. Our aim is to provide the probability law of heat demand given extreme weather conditions, and derive useful implications for the management and production of thermal energy. We propose a copula-based approach and consider the case of the city of Bozen-Bolzano. The analysed data concern daily maxima heat demand observed from January 2014 to November 2017 and the corresponding outdoor temperature. We model the univariate marginal behaviour of the time series of heat demand and temperature with autoregressive integrated moving average models. Next, we investigate the dependence between the residuals' time series through several copula models. The selected copula exhibits heavy-tailed and symmetric dependence. When taking into account the conditional behaviour of heat demand given extreme climatic events, the latter strongly affects the former, and we find a high probability of thermal energy demand reaching its peak. [ABSTRACT FROM AUTHOR]

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor – the robustness (or stability) of the causal chain linking the agent's action and the outcome – influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility (but not of causal strength) increases with the robustness of the action-outcome causal chain. [ABSTRACT FROM AUTHOR]

Copyright of Maintenance & Reliability / Eksploatacja i Niezawodność is the property of Polish Scientific & Technical Society Consumables, Polish Maintenance Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A probabilistic model may involve families of probability functions such that the functions in a family act on a definite (possibly multiple) variable and are indexed by the values of some other (possibly multiple) variable. "Probability kernel" is the term here adopted for referring to any one such family. This study highlights general properties of probability kernels that may be inferred from set-theoretic characteristics of the pairs of variables on which the kernels are defined. In particular, it is shown that any complete set of such pairs of variables has the algebraic form of a lattice, which is then inherited by any complete set of compatible kernels defined on those pairs; that on pairs of variables a criterion may be applied for testing whether corresponding probability kernels are compatible with one another and may thus be the building blocks of a consistent probabilistic model; and that the order between pairs of variables within their lattice provides a general diagnostic about deducibility relations between probability kernels. These results especially relate to models that involve a number of random variables and several interrelated conditional distributions acting on them; for example, hierarchical Bayesian models and graphical models in statistics, Bayesian networks and Markov fields, and Bayesian models in the experimental sciences. [ABSTRACT FROM AUTHOR]

In order to better capture impulsive noises in dynamic systems, a state estimator in the presence of Cauchy distributed process and measurement noises has been studied in recent years. The Cauchy estimator is determined by expressing in closed form the characteristic function (CF) of the unnormalized conditional probability density functions of the states given measurement history. The CF is comprised of a sum of exponentials multiplied by a coefficient, both being nonlinear functions of the measurements and the spectral variable. In this paper, we uncover important properties of the exponential terms in the CF for the two-state Cauchy estimator. These properties can be used to simplify the estimator structure significantly. [ABSTRACT FROM AUTHOR]

Alarm is important in industrial safety management. The technique to capture the correlation information of alarm variables, especially from historical alarm data, is very beneficial for risk prediction and prevention, but the task is very difficult because many alarm tags are associated with a single process variable and often alarm tags are tackled with any specific process variables. In this paper, a general weight-based multi-state sequential algorithm for correlation analysis is applied for alarm data to improve the validity and accuracy of alarm clustering combined with the traditional agglomerative hierarchical clustering algorithm. To make the direction between alarm variables, this paper proposes a vector correlation concept and use conditional probability to measure the alarm correlation among different tags comparable. The method breaks through the limitations of the traditional research on alarm variables correlation that distinguishes sequential alarms with non sequential alarms, or treats differently between regular and irregular alarms. Furthermore, a two-dimensional matrix is used to show the vector correlation of alarm variables intuitively and visually. The data mining algorithm is shown to be able to find out the vector correlation of alarm variables effectively and correctly when applied in the analysis of power plant alarm data. [ABSTRACT FROM AUTHOR]

Copyright of Military Technical Courier / Vojnotehnicki Glasnik is the property of Military Technical Courier / Vojnotehnicki Glasnik and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we are interested in a hypothesis testing problem within the framework of a Generalized Pareto Distribution (GPD) model with interval censoring. For this purpose, we first develop the calculation of the likelihood function, using conditional probabilities, to achieve the same expression proposed by Klein and Moeschberger. Next, we show that the properties of the maximum pseudo-likelihood estimates of the model parameters and essentially the asymptotic normality are preserved. Finally, we built a hypothesis testing to compare two types of breast cancer treatment as part of the model mentioned above. As a result, we can distinguish which treatment lengthens the comfort time of the patients. [ABSTRACT FROM AUTHOR]

Surface transportation networks are critical infrastructure that are frequently affected by volcanic ash fall. Disruption to surface transportation from volcanic ash is often complex with the severity of impacts influenced by a vast array of parameters including, among others, ash properties such as particle size and deposit thickness, meteorological conditions, pavement characteristics, and mitigation actions. Fragility functions are used in volcanic risk assessments to express the conditional probability that an impact or loss state will be reached or exceeded for a given hazard intensity. Most existing fragility functions for volcanic ash adopt ash thickness as the sole hazard intensity metric that determines thresholds for functional loss. However, the selection of appropriate hazard intensity metrics has been highlighted as a crucial factor for fragility function development and recent empirical evidence suggests that ash thickness is not always the most appropriate metric. We review thresholds of functional loss for existing published surface transportation (i.e. road rail, maritime and airport) fragility functions that use ash thickness. We then refine these existing functions through the application of results from a series of recent laboratory experiments, which investigate the impacts of volcanic ash on surface transportation. We also establish new fragility thresholds and functions, which applies ash-settling rate as a hazard intensity metric. The relative importance of alternative hazard intensity metrics to surface transportation disruption is assessed with a suggested approach to account for these in existing fragility functions. Our work demonstrates the importance of considering ash-settling rate, in addition to ash thickness, as critical hazard intensity metrics for surface transportation, but highlights that other metrics, especially particle size, are also important for transportation. Empirical datasets, obtained from both post-eruption field studies and additional laboratory experimentation, will provide future opportunities to refine fragility functions. Our findings also justify the need for rapid and active monitoring and modelling of various ash characteristics (i.e. not ash thickness alone) during volcanic eruptions, particularly as potential disruption to surface transportation can occur with only ~ 0.1 mm of ash accumulation. [ABSTRACT FROM AUTHOR]

Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure. [ABSTRACT FROM AUTHOR]

Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any--per assumption non-existing--heterogeneity. Quantile estimators are nevertheless useful for testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the Kolmogorov-Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is satisfactory and their power sufficient to detect deviations under plausible data-generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected. [ABSTRACT FROM AUTHOR]

We study the case in which the values of random variables increase without bound. [ABSTRACT FROM AUTHOR]

The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information. The optimal update is obtained by minimizing either the Hellinger distance or the quadratic Bregman divergence. The results obtained by the two methods differ. Updates with information about conditional probabilities are considered as well. [ABSTRACT FROM AUTHOR]