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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]

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]

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]

• 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]

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 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]

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]