# Inference and Learning in Hybrid Bayesian Networks

### Kevin P. Murphy

###
EECS Department

University of California, Berkeley

Technical Report No. UCB/CSD-98-990

January 1998

### http://www.eecs.berkeley.edu/Pubs/TechRpts/1998/CSD-98-990.pdf

We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.

BibTeX citation:

@techreport{Murphy:CSD-98-990, Author = {Murphy, Kevin P.}, Title = {Inference and Learning in Hybrid Bayesian Networks}, Institution = {EECS Department, University of California, Berkeley}, Year = {1998}, Month = {Jan}, URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1998/5553.html}, Number = {UCB/CSD-98-990}, Abstract = {We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.} }

EndNote citation:

%0 Report %A Murphy, Kevin P. %T Inference and Learning in Hybrid Bayesian Networks %I EECS Department, University of California, Berkeley %D 1998 %@ UCB/CSD-98-990 %U http://www.eecs.berkeley.edu/Pubs/TechRpts/1998/5553.html %F Murphy:CSD-98-990