The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

This handbook remains the foremost resource for designing hydraulic systems and selecting
hydraulic fluids used in engineering applications. Featuring new illustrations, data tables …

In many complex, real‐world situations, problem solving and decision making require
effective reasoning about causation and uncertainty. However, human reasoning in these …

Developing efficient strategies for searching larger Bayesian networks in exact structure
learning is an open challenge. In this study, ancestral and heuristic partition constraints are …

Purely data-driven methods often fail to learn accurate conditional probability table (CPT)
parameters of discrete Bayesian networks (BNs) when training data are scarce or …

The R package abn is designed to fit additive Bayesian network models to observational
datasets and contains routines to score Bayesian networks based on Bayesian or …

Bayesian networks have many applications. Learning the optimal structure of a Bayesian
network has always been important in this respect. In this paper, a bidirectional heuristic …

One of the most effective structure-learning methods in a Bayesian network is the K2
algorithm. Because the performance of the K2 algorithm depends on the node ordering …

In many complex, real-world situations, problem solving and decision making require
effective reasoning about causation and uncertainty. However, human reasoning in these …

This paper presents an approximate algorithm to obtain a posteriori intervals of probability,
when available information is also given with intervals. The algorithm uses probability trees …