Planning for Concurrent Action Executions Under Action Duration Uncertainty Using Dynamically Generated Bayesian Networks

Many important factors affect the spread of childhood infectious disease. To better understand the fundamental drivers of infectious disease spread, several researchers have estimated seasonal transmission coefficients in discrete-time models. In this paper, we build upon this previous work and also develop a framework for efficient estimation using continuous differential equation models. We introduce nonlinear programming formulations to efficiently estimate model parameters and seasonal transmission profiles from existing case count data for the childhood disease, measles. We compare results from discrete time and continuous time models and address several shortcomings of the discrete-time method, including removing the need for the data reporting interval to match the time between successive cases in the chain of transmission or serial interval of the disease. Using a simultaneous approach for optimization of differential equation systems, we demonstrate that seasonal transmission parameters can be effectively estimated using continuous time models instead of discrete-time models.

Preference-Free Option Pricing with Path-Dependent Volatility: A Closed-Form Approach

Autoparametric Vibrations of a Nonlinear System with a Pendulum and Magnetorheological Damping, by Jerzy Warminski and Krzysztof Kecik: Introduction to Autoparametric Vibrations. - Model of the Nonlinear Oscillator with an Attached Pendulum. - Approximate Analytical Solutions and Their Stability. - Regular and Chaotic Dynamics Near the Main Parametric Resonance. - Experimental Setup of the System with Active MR Damper. - Dynamics of an Autoparametric System with MR Damper. - Influence of a Nonlinear System's Suspension on the InstabilityRegions. - Conclusions and Remarks. - References. - On the Dynamics of Pedestrians-Induced Lateral Vibrations of Footbridges, by Stefano Lenci and Laura Marcheggiani: Introduction and Literature Review. - A Continuous-Time Model: the SAMEO Model. - A Discrete-Time Model. - Conclusions. - References. - Applications for Shape Memory Alloys in Structural and Machine Dynamics, by Matthew P. Cartmell, Arkadiusz J. Z ak and Olga A. Ganilova: Review of the Literature and Introduction. - Modelling of the Shape Memory Effect. - Dynamics of Composite Beams and Plates with Integrated SMA Elements. - Applications to Flexible Rotors. - Antagonistic Actuation Control of Vibration in Plates. - Conclusions. - References. - Theoretical and Experimental Nonlinear Vibrations of Sagged Elastic Cables, by Giuseppe Rega: Introduction. - Cable Modelling and Theoretical Analysis. - Nonlinear Phenomena in Forced Dynamic Response. - Experimental Characterisation of Cable Nonlinear Dynamics. - Further Developments and Research Topics. - References. - Engineering Applications of Non-Smooth Dynamics, by Marian Wiercigroch and Ekaterina Pavlovskaia Non-Smooth Dynamical Systems in Engineering Dynamics. - Drifting Oscillator as an Effective Model of Vibro-Impact Moling. - Nonlinear Dynamics Caused by Fatigue Crack Growth. - Regular and Chaotic Dynamics of a Rotor System with a Bearing Clearance. - Conclusions. - References.

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

New distribution theory for the estimation of structural break point in mean

To take full advantage of the historical information, we adopt dynamic Bayesian networks and discrete-time Bayesian networks to describe the event trees or dynamic fault trees. On this basis of the regulation, corresponding dynamic Bayesian networks and discrete-time Bayesian networks models are established for safety assessment in timing system. When the discrete-time time Bayesian network of dynamic fault trees is acquired, the Bayesian network is used to study the consequences probability, key degree and computation method. A simulated banking transaction system is analyzed and assessed by above models. The results show that the safety assessment scheme can provide model without subjectivity, which raises the credibility of model. In addition, it can obtain general assessment results and acquire more extensive diagnostics and information.

Continuous-time (CT) models are a flexible approach for modeling longitudinal data of psychological constructs. When using CT models, a researcher can assume one underlying continuous function for the phenomenon of interest. In principle, these models overcome some limitations of discrete-time (DT) models and allow researchers to compare findings across measures collected using different time intervals, such as daily, weekly, or monthly intervals. Theoretically, the parameters for equivalent models can be rescaled into a common time interval that allows for comparisons across individuals and studies, irrespective of the time interval used for sampling. In this study, we carry out a Monte Carlo simulation to examine the capability of CT autoregressive (CT-AR) models to recover the true dynamics of a process when the sampling interval is different from the time scale of the true generating process. We use two generating time intervals (daily or weekly) with varying strengths of the AR parameter and assess its recovery when sampled at different intervals (daily, weekly, or monthly). Our findings indicate that sampling at a faster time interval than the generating dynamics can mostly recover the generating AR effects. Sampling at a slower time interval requires stronger generating AR effects for satisfactory recovery, otherwise the estimation results show high bias and poor coverage. Based on our findings, we recommend researchers use sampling intervals guided by theory about the variable under study, and whenever possible, sample as frequently as possible. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The paper presents a Bayesian networks (BN) based method to analyze the reliability of phased-mission systems (PMS). The method includes three steps. Firstly, each phase of PMS is represented by a BN framework, named phase-BN. Then, in order to express the dependences across phases, all the phase-BN are combined by (1) connecting the root nodes that represent the same component but belong to different phases, and (2) connecting the leaf nodes of the phase-BN with a new node that represents the whole PMS mission. The new constructed BN is named PMS-BN. Lastly, the reliability analysis is performed by a discrete-time BN modeling acting on PMS-BN. Two examples are used to expatiate on the proposed approach. The PMS-BN based method provides a new way to analyze PMS, especially those with dynamic phases. Further, based on PMS-BN, fault diagnoses and sensitivity analysis can be performed easily.

For several specific queueing models with a vacation policy, the stationary system occupancy at the beginning of a random slot is distributed as the sum of two independent random variables. One of these variables is the stationary number of customers in an equivalent queueing system with no vacations. For models in continuous time with Poissonian arrivals, this result is well-known, and referred to as stochastic decomposition, with proof provided by Fuhrmann and Cooper. For models in discrete time, this result received less attention, with no proof available to date. In this paper, we first establish a proof of the decomposition result in discrete time. When compared to the proof in continuous time, conditions for the proof in discrete time are somewhat more general. Second, we explore four different examples: non-preemptive priority systems, slot-bound priority systems, polling systems, and fiber delay line (FDL) buffer systems. The first two examples are known results from literature that are given here as an illustration. The third is a new example, and the last one (FDL buffer systems) shows new results. It is shown that in some cases the queueing analysis can be considerably simplified using this decomposition property.

Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for example, in other sensory modalities.

In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.

A method for solving the fault diagnosis problem of air brake system based on probabilistic approach is presented. The fault diagnosis model based on Bayesian network was built for the uncertainty characteristic of fault in the air brake system. Through evaluating the characteristic of Bayesian networks in the diagnosis inference and model expression, it is demonstrated that this method can solve the uncertain problems in fault diagnosis. The test result has shown that the Bayesian network model is effective in fault diagnosis of the air brake system.

Inrecent years, the rapid increase of electric vehicles (EVs) has significantly enhanced the potential for flexible regulation capabilities (FRC) within the power grid. However, the stochastic nature of EV charging behavior poses significant challenges to the accurate evaluation and prediction of FRC. This study introduces a probabilistic prediction method that employs Bayesian Neural Networks combined with Transformer models to achieve FRC prediction under uncertainty. Initially, battery capacity and energy demand for EVs are considered to quantify the FRC of an individual EV, with the aggregated regulation capability obtained using the Minkowski sum method. Subsequently, feature engineering techniques are applied to identify features associated with the flexibility of EVs. Finally, a prediction model is constructed based on the Transformer architecture to forecast FRC of EVs. This model incorporates the ideas of Bayesian Neural Networks by defining the weights and bias parameters in the neural network as probability distributions, thus achieving probabilistic prediction. This study conducts experimental validation using real-world data, and the results demonstrate that the proposed method outperforms other algorithms in terms of prediction accuracy while effectively capturing uncertainty.

As an extension of the well-known Action Description languageA introduced by M. Gelfond and V. Lifschitz [7], C. Baral and M. Gelfond recently defined the dialect A C which allows the description of concurrent actions [1]. Also, a sound but incomplete encoding of A C by means of an extended logic program was presented there. In this paper, we work on interpretations of contradictory inferences from partial action descriptions. Employing an interpretation different from the one implicitly used in A C , we present a new dialect A C + , which allows to infer non-contradictory information from contradictory descriptions and to describe nondeterminism and uncertainty. Furthermore, we give the first sound and complete encoding of A C , using equational logic programming, and extend it to A C + as well.

As an extension of the well-known Action Description languageA introduced by M. Gelfond and V. Lifschitz [7], C. Baral and M. Gelfond recently defined the dialect A C which allows the description of concurrent actions [1]. Also, a sound but incomplete encoding of A C by means of an extended logic program was presented there. In this paper, we work on interpretations of contradictory inferences from partial action descriptions. Employing an interpretation different from the one implicitly used in A C , we present a new dialect A C + , which allows to infer non-contradictory information from contradictory descriptions and to describe nondeterminism and uncertainty. Furthermore, we give the first sound and complete encoding of A C , using equational logic programming, and extend it to A C + as well.