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#### Institute

Probability propagation nets
(2008)

This work introduces a Petri net representation for the propagation of probabilities and likelihoods, which can be applied to probabilistic Horn abduction, fault trees, and Bayesian networks. These so-called "probability propagation nets" increase the transparency of propagation processes by integrating structural and dynamical aspects into one homogeneous representation. It is shown by means of popular examples that probability propagation nets improve the understanding of propagation processes - especially with respect to the Bayesian propagation algorithms - and thus are well suited for the analysis and diagnosis of probabilistic models. Representing fault trees with probability propagation nets transfers these possibilities to the modeling of technical systems.

Probability propagation nets
(2007)

A class of high level Petri nets, called "probability propagation nets", is introduced which is particularly useful for modeling probability and evidence propagation. These nets themselves are well suited to represent the probabilistic Horn abduction, whereas specific foldings of them will be used for representing the flows of probabilities and likelihoods in Bayesian networks.