Bayesian Networks in the Absence of Temporal Information

Background. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting upper and lower motor neurons in the brain and spinal cord. The heterogeneity in the course of ALS clinical progression and ultimately survival, coupled with the rarity of this disease, make predicting disease outcome at the level of the individual patient very challenging. Besides, stratification of ALS patients has been known for years as a question of great importance to clinical practice, research and drug development. Methods. In this work, we present a Dynamic Bayesian Network (DBN) model of ALS progression to detect probabilistic relationships among variables included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), which provides records of over 10,700 patients from different clinical trials, and with over 2,869,973 longitudinally collected data measurements. Results. Our model unravels new dependencies among clinical variables in relation to ALS progression, such as the influence of basophil count and creatine kinase on patients’ clinical status and the respiratory functional state, respectively. Furthermore, it provided an indication of ALS temporal evolution, in terms of the most probable disease trajectories across time at the level of both patient population and individual patient. Conclusions. The risk factors identified by out DBN model could allow patients' stratification based on velocity of disease progression and a sensitivity analysis on this latter in response to changes in input variables, i.e. variables measured at diagnosis.

The thesis commences with a review of basic Static Bayesian Networks (SBNs) and describesnsome of the methods of probabilistic inference from SBNs, including Pearl's causal treenmethodology and the more general NP-complete clique tree methodology due to Lauritzennand Speigelhalter. The use of SBNs in pattern classification will then be described withnspecial consideration given to the use of elementary forms of SBNs including the NaivenBayes and the Tree Augmented Naїve Bayes (TAN) classifiers. The possibility of applyingnboosting and other ensemble methods to these elementary forms in order to obtain superiornperformance will then be explored. The discussion will conclude by investigating thensuitability of correlation measures in computing the TAN as an initial step in adapting it tonsolve the forecasting problem. Attention will then be turned to the use of Bayesian Networksn(BN) in forecasting. Numerous techniques have been developed to create accurate forecastingnmodels and the BN approach, with a time element incorporated, is among the more successfulnof these. The time element will be introduced by means of a series of SBNs, acting as time-slicesnto create a Dynamic Bayesian Network (DBN), to solve the forecasting problem. Thensuitably modified TAN combined with the Pearl causal tree, now called the TAN-PearlnNetwork (TPN), will form the basis of the DBN. The objective of the forecasting problemnwill be to minimize overall error, computed from the differences between the computednbeliefs at each time-slice, and those of the actual events. The final major investigationnconcerns the application of boosting to regression. It draws upon the parallel between time-slicesnin a DBN and instances in regression analysis and it will be shown that the accuracy ofnthe time-slices, and therefore that of the DBN as a whole, can be improved through boosting.n

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.

This paper presents a comparative study of availability evaluation of systems modeled by a mixed fault tree (MFT). This MFT contain a static sub-trees and dynamic sub-trees. These sub-trees can be independent or dependent. To evaluate the availability of a system from its fault tree, three methods are applied here: the first uses the classic Bayesian networks (CBN) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees, the second being proposed, bases on the binary decision diagrams (BDD) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees and the third being proposed uses the binary decision diagram (BDD) for static sub-trees and Markov chains (MC) for dynamic sub-trees . The three methods are compared in both cases; independence and dependence of sub-trees.

Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S.Cerevisiae cell cycle gene expression data. The experimental results demonstrate that the accuracy of our method outperforms the previous work

Driver distraction has become an important and growing safety concern as the use of in-vehicle information systems (IVISs), such as cell phones and navigation systems, continues to increase. One approach to allowing people to benefit from IVISs without compromising safety is to create adaptive IVISs that adjust their functions according to driver and roadway state. A critical element of adaptive IVISs involves monitoring driver distraction in real time; with such a monitoring function it is possible to mitigate that distraction. This study applied Bayesian networks (BNs), a data mining method, to develop a real-time approach to detecting cognitive distraction using drivers’ eye movements and driving performance. Data were collected in a simulator experiment involving 10 participants who interacted with an IVIS system while driving. BN models were trained and tested to investigate the influence of three model characteristics on distraction detection: time history of driver behavior, inclusion of hidden nodes in the model structure, and how data are summarized and the length of training sequences. Results showed that BNs could identify driver distraction reliably with an average accuracy of 80.1%. Dynamic BNs (DBNs) that consider time dependencies of driver behavior produced more sensitive models than static BNs (SBNs). Longer training sequences improved DBN model performance. Blink frequency and fixation measures were particularly indicative of distraction. These results demonstrate that BNs, especially DBNs, can detect driver cognitive distraction by extracting information from complex behavioral data. Potential applications include the design of adaptive in-vehicle systems and the evaluation of driver distraction.

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Anomaly detection in vessel tracks using Bayesian networks

This paper proposes a method to localize a moving agent - such as an autonomous surveillance vehicle - inside a known environment using First Person Viewpoint video data. Anomalies w.r.t. expected vehicle motion and image content are extracted to guide the localization, signal when the localization results are not trustworthy, and explain the reason for the failure. During the training phase, a Dynamic Bayesian Network model is learned, which couples positional and video data. To learn it, clustering is performed on the odometry data, and a modified Kalman Variational Autoencoder is built over the video data. During the testing phase, a Coupled Markov Jump Particle Filter leverages the learned Dynamic Bayesian Network to extract anomalies and to estimate the vehicle’s position, given only camera data. The proposed method is evaluated on two real-world datasets of a vehicle performing perimeter monitoring of a closed environment [17] and of a shopping cart moving in a supermarket

Dynamic Bayesian network modeling for longitudinal brain morphometry

Tracing and prediction analysis of an urban pipeline leakage accident based on the catastrophe DBN model

A Dynamic Bayesian Network model for ship-ice collision risk in the Arctic waters

In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.

IntroductionDynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002–2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with the highest values of mutual information and target entropy reduction, along with the lowest value of target entropy, was considered to have the strongest predictive power in the dataset.ResultsThis study revealed that long-term participation in programs increased the likelihood of children being in a normal nutritional state. Key factors influencing the nutritional status of children under two years of age include the mother’s education level, her subjective well-being, and the household’s wealth quintile. Children with educated parents were more likely to have a healthy nutritional status. Additionally, the causal pathway of intervention programs → wealth quintile → child nutritional status consistently exceeded 90% in Waves 3, 4, and 5, indicating a strong relationship. Similarly, the relationship between intervention programs → food security → child nutritional status was nearly perfect at 99.99% in Waves 4 and 5, indicating a strong association. Finally, the study revealed that household participation in intervention programs significantly reduces undernutrition in best-case scenarios, while the absence of support poses a higher risk in worst-case conditions.ConclusionThe comprehensive intervention program strongly improved household wealth, food security, and maternal well-being, which in turn affected children’s nutritional status.

Modelling based on probabilistic inference can be used to estimate the quality of information delivered by a military sensor network. Different modelling tools have complementary characteristics that can be leveraged to create an accurate model open to intuitive and efficient querying. In particular, stochastic process models can be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The quality of information may be assessed by using the model to compute the probability that reports made by the network to its users are correct. In contrast, dynamic Bayesian network models, which have been used in a variety of military applications, are a more suitable vehicle for understanding the overall network performance and making inferences about the quality of information. Queries can be made simply by instantiating some variables and computing the probability distributions over others. We show that it is possible to combine both modelling tools by constructing a Bayesian network over the state variables of the process algebra model. The sparsity of the resulting Bayesian network allows fast propagation of probabilities, and hence interactive querying for the quality of information.