Dynamic Bayesian Network Structure Research Articles

Integrating Models in Education: Evaluating Strategies and Enhancing Student Learning Through Advanced Analytical Methods

This paper undertakes an in-depth examination of contemporary educational models, focusing on their impact on student learning and teaching strategies. It contrasts static models like the Item Response Theory (IRT) with dynamic models, notably Knowledge Tracing (KT) and its extensions such as the Knowledge Tracing Model (KTM) with a "Tutor Intervention" element. These dynamic models, particularly the dynamic Bayesian network (DBN) structure of KT, provide a detailed perspective on student learning, accommodating temporal skill variations and diverse educational interventions. The study further explores learning decomposition, revealing its effectiveness in evaluating various reading practices and their influence on reading fluency, underscoring the need for personalized educational approaches. Additionally, the paper discusses the Bayesian Evaluation and Assessment framework within Intelligent Tutoring Systems (ITS), offering a holistic view of both the immediate and long-term effects of tutoring. Key insights are presented on the efficacy of different educational models in reading education, advocating for diversified reading practices and personalized learning strategies. The paper also identifies limitations in current models, such as high standard errors in learning decomposition and weak fits in models like LR-DBN, and proposes future research directions, including automated analysis and a hybrid approach combining human expertise with computational analysis. This approach aims to enhance educational data mining and inform effective educational strategies and outcomes.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

A Novel Approach for Modeling and Evaluating Road Operational Resilience Based on Pressure-State-Response Theory and Dynamic Bayesian Networks

Urban roads face significant challenges from the unpredictable and destructive characteristics of natural or man-made disasters, emphasizing the importance of modeling and evaluating their resilience for emergency management. Resilience is the ability to recover from disruptions and is influenced by factors such as human behavior, road conditions, and the environment. However, current approaches to measuring resilience primarily focus on the functional attributes of road facilities, neglecting the vital feedback effects that occur during disasters. This study aims to model and evaluate road resilience under dynamic and uncertain emergency event scenarios. A new definition of road operational resilience is proposed based on the pressure-state-response theory, and the interaction mechanism between multidimensional factors and the stage characteristics of resilience is analyzed. A method for measuring road operational resilience using Dynamic Bayesian Networks (DBN) is proposed, and a hierarchical DBN structure is constructed based on domain knowledge to describe the influence relationship between resilience elements. The Best Worst method (BWM) and Dempster–Shafer evidence theory are used to determine the resilience status of network nodes in DBN parameter learning. A road operational resilience cube is constructed to visually integrate multidimensional and dynamic road resilience measurement results obtained from DBNs. The method proposed in this paper is applied to measure the operational resilience of roads during emergencies on the Shanghai expressway, achieving a 92.19% accuracy rate in predicting resilient nodes. Sensitivity analysis identifies scattered objects, casualties, and the availability of rescue resources as key factors affecting the rapidity of response disposal in road operations. These findings help managers better understand road resilience during emergencies and make informed decisions.

Dynamic Bayesian Networks for Feature Learning and Transfer Applications in Remaining Useful Life Estimation

Prognostics and health management (PHM) is one of the research hotspots in reliability, where remaining useful life (RUL) estimation is a typical application scenario. In this article, a feature learning method based on dynamic Bayesian networks (DBNs) is proposed to improve the RUL estimation accuracy of the regression models. The best feature set is obtained with the conditional dependencies represented by the DBN structure. A local modeling method is applied here to reduce the computation for high-order DBN construction. The strength of the connections between variables together with a contribution index of variables in the DBN structure are defined to represent the feature importance of the variables. Feature transfer is carried out with feature importance under different operating conditions for a further improvement. Nonlinear regression models such as support vector regression (SVR) and Gaussian mixture regression (GMR) are built based on the learned features and used to estimate the RUL. The turbofan engine dataset C-MAPSS is used to validate the effectiveness of the proposed method. Compared with other recent RUL estimation models, the proposed method has a faster modeling speed and higher prediction accuracy.

Research on run-time risk evaluation method based on operating scenario data for autonomous train

Recent years witness the focus of the research of next-generation railways on risk situation awareness and safety decision-making to enhance the autonomy of unmanned trains. However, complex environmental factors make it difficult to assess the risks of train operation. Thus, it is of great necessity to clearly monitor the scenario parameters under which the train control system is designed to work, and to infer real-time risk through the collected scenario data. This paper first clarifies the key scenario parameters that need to be collected during the operation according to the concept of Operational Design Domain (ODD) and operating scenario. The key parameters and their dependencies are used to derive the Dynamic Bayesian Network (DBN) structure. Second, for data probability uncertainty, Fuzzy Set Theory is introduced, within the framework of which a fuzzy dynamic reasoning process is presented by monitoring the scenario data deviation. Finally, a case of real-time risk evaluation and analysis of the accident of Singapore MTR is explicated to demonstrate its contribution to operating data-based runtime risk analysis.

Risk Coupling Analysis of Deep Foundation Pits Adjacent to Existing Underpass Tunnels Based on Dynamic Bayesian Network and N–K Model

Because deep foundation pits and tunnels are deformation-sensitive structures, the safety of these projects is generally affected by coupled risks. In deep foundation pit construction, if the existing tunnel structure adjacent to the deposit is damaged, it can produce a severe group disaster. It is necessary to identify an efficient risk analysis model to study the dynamic coupled risk of deep foundation pit projects adjacent to existing underpass tunnels and to analyze the risk evolution law to achieve effective real-time safety control. This study proposes a coupled risk analysis model using the N–K model and dynamic Bayesian network to construct deep foundation pits in adjacent existing underpass tunnels. The model is predicated on association rules to explore the interrelationship between risk factors to build a dynamic Bayesian network structure. In addition, the N–K model is utilized to quantify coupled risks under such complex working conditions and to optimize the dynamic Bayesian network structure. The developed model clarifies the risk coupling mechanism of deep foundation pit construction adjacent to an existing underpass tunnel, finds the critical points in the risk transfer process, and conducts dynamic risk prediction and accident causation diagnosis for the coupled risk to realize the dynamic control of the coupled risk in the adjacent existing underpass tunnel construction. Taking the Nanning underground comprehensive utilization project as an example, the validity and applicability of the proposed approach were tested. The results showed that the model is feasible and has application potential, providing effective decision support for safety control while constructing deep foundation pits adjacent to existing tunnels.

Dynamic Bayesian Network Learning to Infer Sparse Models From Time Series Gene Expression Data.

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN, along with the documentation and tutorials.

A dynamic programming Bayesian network structure learning algorithm based on mutual information

Existing Bayesian network (BN) structure learning algorithms based on dynamic programming have high computational complexity and are difficult to apply to large-scale networks. Therefore, this paper proposes a Dynamic Programming BN structure learning algorithm based on Mutual Information, the MIDP (Dynamic Programming Based on Mutual Information) algorithm. The algorithm uses mutual information to build the maximum spanning tree and [Formula: see text]-order matrix, and introduces a penalty coefficient d based on the matrix-based node removal strategy, so as to reduce the number of scoring calculations and time consumption of the algorithm. Simulation results show that, compared with DP, SMDP and MEDP algorithms, the MIDP algorithm can reduce the calculation times and time consumption of algorithm scores while maintaining the accuracy of the algorithm when selecting the appropriate [Formula: see text] value.

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model.

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.

Using DBN and evidence-based reasoning to develop a risk performance model to interfere ship navigation process safety in Arctic waters

Risk performance reasoning strategy for LNG ships navigating in Arctic waters is proposed in this paper. Many uncertainties exist in the reasoning of ship navigation risk in Arctic waters, which are influenced by multi-source risk causing events. As a result of the aforementioned concerns, the dynamic Bayesian network (DBN) structure is offered as a solution for an uncertain risk assessment model. The DBN network can benefit from a strategy for solving ambiguous data information based on Dempster–Shafer (D–S) evidence theory and a cloud model. Additionally, a risk performance reasoning technique for LNG tanker collision accidents in Arctic waters is developed. Besides, the marine meteorological reanalysis data, data from ship-borne sensor monitoring, and expert knowledge in the suggested risk performance reasoning method are incorporated. A case study confirmed that the risk performance reasoning of accidents was needed revealed that the main risk in Arctic summer waters is posed by obstacles in the channel that are difficult to detect, such as icebergs and reefs.

The engineering skills training process modeling using dynamic bayesian nets

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

Inferring signaling pathways with probabilistic programming.

MotivationCells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.ResultsWe implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.Availability and implementationFind the full codebase at https://github.com/gitter-lab/ssps.Supplementary information Supplementary data are available at Bioinformatics online.

Learning Dynamic Bayesian Networks structure based on a new hybrid K2-Bat learning algorithm

ABSTRACT The temporal dimension makes it difficult and complex to learn the Dynamic Bayesian Networks structure for huge search space. We propose a new hybrid K2-Bat algorithm to learn the structure of Dynamic Bayesian Networks. This work contains two optimal strategies: an ordering-based algorithm INOK2 to learn initial network structure and an adaptive binary bat algorithm to learn transition network structure. Based on the requirement of K2 algorithm for prior knowledge, a fitness function is built to quantitatively score node order in INOK2. The initial population is generated by the node block sequence constructed by directional support tree. A dynamic learning factor, inverted mutation s`node sequence to improve the global searching ability and the convergence speed. Then, the optimal initial network structure can be obtained. In addition, an improved binary bat algorithm is proposed to improve the development behavior of bat algorithm by using dynamic selection strategy in transitional network learning. Finally, experiments on four well-known benchmark problems are performed. The results show that the proposed algorithm can successfully learn the structure of Dynamic Bayesian Networks without prior knowledge, and balance solutions quality and computational effort.

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.

Dynamic Bayesian network state prediction based on variable relationship

In order to improve the accuracy of the state prediction model, a dynamic Bayesian network state prediction model based on the relationship of prediction variables is designed. The prediction model of dynamic Bayesian network structure learning algorithm was improved, integrated into the Gibbs sampling algorithm model prediction, joined the predicted relationship between different factors affecting the node, is given based on the variable relationship between the dynamic Bayesian network structure design, using a moment on the different nodes and state influence factors to predict the probability distribution of the moment state nodes. The experimental results show that the model is simple in structure, more accurate than the traditional learning method of Bayesian network structure, and more practical.

Discerning Functional Connections in the Pulsed Neural Networks with the Dynamic Bayesian Network Structure Search Method Based on a Genetic Algorithm.

It is important to explore potential structural characteristics of biological networks and regulatory mechanisms of network behaviors at the system level. In this study, a dynamic Bayesian network structure search method (DBNSSM) based on a genetic algorithm is employed to infer and locate functional connections in pulsed neural networks (PNNs) as typical artificial neural networks. In the process of network structure searching, a minimum description length score is calculated for each candidate network structure. The score indicates two characteristics of the network structure: (1) the likelihood based on network dynamic response data and (2) the complexity. Both should be considered together on selecting network structures. The DBNSSM is applied to analyze time-series data from PNNs, thereby discerns functional connections showing network structures collectively. It is feasible to analyze multichannel electrophysiological data of biological neural networks using the DBNSSM.

Speech-driven animation with meaningful behaviors

Conversational agents (CAs) play an important role in human computer interaction (HCI). Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, and (2) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained baseline model.

Разработка гибридного алгоритма обучения структуры динамической байесовской сети на основе метода Левенберга – Марквардта

Азарнова Татьяна Васильевна, д-р техн. наук, зав. кафедрой математических методов исследования операций, Воронежский государственный университет, г. Воронеж; ivdas_92@mail.ru. Баркалов Сергей Алексеевич, д-р техн. наук, декан факультета экономики, менеджмента и информационных технологий, зав. кафедрой управления строительством, Воронежский государственный технический университет, г. Воронеж; sbarkalov@nm.ru. Полухин Павел Валерьевич, канд. техн. наук, преподаватель, Воронежский государственный университет, г. Воронеж; alfa_force@bk.ru. T.V. Asarnova1, ivdas_92@mail.ru, S.A. Barkalov2, sbarkalov@nm.ru, P.V. Polukhin1, alfa_force@bk.ru 1 Voronezh State University, Voronezh, Russian Federation, 2 Voronezh State Technical University, Voronezh, Russian Federation

A New Approach to Learning in Dynamic Bayesian Networks (DBNs)

In this paper, we revisit the parameter learning problem, namely the estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are directed graphical models of stochastic processes that encompasses and generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs). Whenever we apply these models to economics and finance, we are forced to make some modeling assumptions about the state dynamics and the graph topology (the DBN structure). These assumptions may be incorrectly specified and contain some additional noise compared to reality. Trying to use a best fit approach through maximum likelihood estimation may miss this point and try to fit at any price these models on data. We present here a new methodology that takes a radical point of view and instead focus on the final efficiency of our model. Parameters are hence estimated in terms of their efficiency rather than their distributional fit to the data. The resulting optimization problem that consists in finding the optimal parameters is a hard problem. We rely on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to tackle this issue. We apply this method to the seminal problem of trend detection in financial markets. We see on numerical results that the resulting parameters seem less error prone to over fitting than traditional moving average cross over trend detection and perform better. The method developed here for algorithmic trading is general. It can be applied to other real case applications whenever there is no physical law underlying our DBNs.

Dynamic Bayesian network threat assessment for warship formation: a data analysis method

In the target threat assessment of maritime formation air defence, the observation data are easy to be missing, and existing data analysis methods are difficult to carry out dynamic assessment in time series. In order to solve these problems, a data analysis method about threat assessment is proposed, which is based on discrete dynamic Bayesian networks (DDBN) and the utility theory. Firstly, the data characteristics of the target threat assessment are analysed, and a two-stage dynamic Bayesian network structure evaluation system is constructed. Secondly, the continuous variable in the network structure is transformed into a discrete variable, which can avoid the repeated calculation caused by the continuous change of the node threat attribute value in a small range. Then, the prior probability of the credibility of the uncertainty node to make the Bayesian network parameters more realistic, and the utility theory is introduced to carry out the threat ranking. Finally, the simulation results show that the data analysis method is in good agreement with the artificial judgment. This proposed method has a certain practical significance, which realises the data processing of dynamic threat assessment.