Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In ...

Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. This article discusses the properties of the modelling framework that are of highest importance for reliability practitioners.

Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not...

Bayesian networks (BNs) are used to represent and efficiently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagation in a clique tree compiled from a Bayesian network. There is a lack of understanding of how cl...

Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs. We briefly present the theory of BNs and introduce our probabilistic model. We also pres...

This overview article for the special series, "Bayesian Networks in Environmental and Resource Management," reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource manageme...

Causation plays a critical role in many predictive and inference tasks. Bayesian networks (BNs) have been used to construct inference systems for diagnostics and decision making. More recently, fuzzy cognitive maps (FCMs) have gained considerable attention and offer an alternative framework for representing structured human knowledge and causal inference. In this paper I briefly introduce Bayes...

We present a technique for incorporating data attributes that are supposed Missing Not at Random (MNAR) into Bayesian Networks (BNs). While traditional methods of incorporating data that is Missing at Random (MAR) into BNs are well documented, there are fewer tested methods for discovering and incorporating data Missing Not at Random (MNAR). We present a review of literature in BNs and missing ...

Probabilistic expert systems based on Bayesian networks (BNs) require initial specification both a qualitative graphical structure and quantitative assessment of conditional probability tables. This paper considers statistical batch learning of the probability tables on the basis of incomplete data and expert knowledge. The EM algorithm with a generalized conjugate gradient acceleration method ...

We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a structural and temporal extension of Bayesian Belief Networks (BNs) to facilitate normative temporal and causal modeling under uncertainty. In this paper we present definitions of the model, its components, and its fundamental properties. We also discuss how to represent various types of temporal knowledge, with an em...