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Learning the Structure of Dynamic Probabilistic Networks
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend struc…
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Kevin Murphy, Yair Weiss, Michael I. Jordan
Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dr…
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Kevin Murphy
We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Ess…
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Arnaud Doucet, Nando de Freitas, Kevin Murphy +1
Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob…
The Factored Frontier Algorithm for Approximate Inference in DBNs
Kevin Murphy, Yair Weiss
The Factored Frontier (FF) algorithm is a simple approximate inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar tothe fully factorized version of the Boyen…