1.5k citations · 2.4k across the 5 of their papers we have counts for
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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…
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…