So causal models need to be able to treat causal relationships as causes and effects. This observation motivates an extension the Bayesian network causal calculus (Section 2) to allow nodes that themselves take Bayesian networks as values. Such networks will be called recursive Bayesian networks (Section 3). Because recursive Bayesian networks make causal and probabilistic claims at different l...

One of the most common problems encountered in agriculture is that of predicting a response variable from covariates of interest. The aim of this paper is to use a Bayesian neural network approach to predict dairy daughter milk production from dairy dam, sire, herd and environmental factors. The results of the Bayesian neural network are compared with the results obtained when the regression re...

To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the op...

Inference in Bayesian networks is used to calculate the posterior probability distributions of unobserved variables in a network. These posterior probability distributions are used to draw conclusions and are the basis for decisions, in the domain of a particular model. Inference is a complex process and can be difficult to understand for even the most experienced Bayesian network users. In thi...

This paper presents a Dynamic Bayesian Network (DBN)-based distributed diagnosis scheme, where each distributed diagnoser generates globally correct diagnosis results without a centralized coordinator by communicating a minimal number of measurements so that each diagnoser satisfies local observability properties, and the overall diagnoser is globally observable. We present a procedure for desi...

A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for bayesian network learning. Some of them are based on search and score methodology and the others follow an information theory based approach. One of the most known algorithm for learning bayesian network is the SLA alg...

This paper is about helping people who make critical decisions improve the quality of their judgements. We provide a brief introduction to Bayesian Nets (BNs) and use an example in safety assessment. We show how BNs enable decision-makers to combine different types of evidence (including subjective judgements) to provide quantitative, auditable arguments. By using state-ofthe-art BN technology ...

In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of attention in the statistics literature. In this paper, we take a well-known method from statistics, Efron’s Bootstrap, and examine its applicability for assessing a confidence measure on features o...