Corrected score methods for estimating Bayesian networks with error?prone nodes

A comparison of score-based methods for estimating Bayesian networks using the Kullback-Leibler divergence

In this paper, we compare the performance of two methods for estimating Bayesian networks from data containing exogenous variables and random effects. The first method is fully Bayesian in which a prior distribution is placed on the exogenous variables, whereas the second method, which we call the residual approach, accounts for the effects of exogenous variables by using the notion of restrict...

Learning Temporal Nodes Bayesian Networks

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Bayesian Methods for Neural Networks

Using Hidden Nodes in Bayesian Networks

In the construction of a Bayesian network, it is always assumed that the variables starting from the same parent are conditionally independent. In practice, this assumption may not hold, and will give rise to incorrect inferences. In cases where some dependency is found between variables, we propose that the creation of a hidden node, which in effect models the dependency, can solve the problem...

Generalizing Continuous Time Bayesian Networks with Immediate Nodes

An extension to Continuous Time Bayesian Networks (CTBN) called Generalized CTBN (GCTBN) is presented; the formalism allows one to model, in addition to continuous time delayed variables (with exponentially distributed transition rates), also non delayed or “immediate” variables, which act as standard chance nodes in a Bayesian Network. The usefulness of this kind of model is discussed through ...