Authors: Kurt Lautenbach Alexander Pinl
Publish Date: 2009/06/21
Volume: 10, Issue: 2, Pages: 683-709
Abstract
This article combines Bayes’ theorem with flows of probabilities flows of evidences likelihoods and fundamental concepts for learning Bayesian networks as biological models from data There is a huge amount of biological applications of Bayesian networks For example in the fields of protein modeling pathway modeling gene expression analysis DNA sequence analysis protein–protein interaction or protein–DNA interaction Usually the Bayesian networks have to be learned statistically constructed from array data Then they are considered as an executable and analyzable model of the data source To improve that this work introduces a Petri net representation for the propagation of probabilities and likelihoods in Bayesian networks The reason for doing so is to exploit the structural and dynamic properties of Petri nets for increasing the transparency of propagation processes Consequently the novel Petri nets are called “probability propagation nets” By means of examples it is shown that the understanding of the Bayesian propagation algorithm is improved This is of particular importance for an exact visualization of biological systems by Bayesian networks
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