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Kevin P Murphy

2002/11/12

Internet, November

A Tutorial on Dynamic Bayesian Networks Page 1 A Tutorial on Dynamic Bayesian Networks Kevin P. Murphy MIT AI lab 12 November 2002 Page 2 Modelling sequential data • Sequential data is everywhere, eg, – Sequence data (offline): Biosequence analysis, text processing, ... – Temporal data (online): Speech recognition, visual tracking, financial forecasting, ... • Problems: classification, segmentation, state estimation, fault diagnosis, prediction, ... • Solution: build/learn generative models, then compute P(quantity of interest|evidence) using Bayes rule. 1 Page 3 Outline of talk • Representation – What are DBNs, and what can we use them for? • Inference – How do we compute P(Xt|y1:t ) and related quantities? • Learning – How do we estimate parameters and model structure? 2 Page 4 Representation • Hidden Markov Models (HMMs). • Dynamic Bayesian Networks (DBNs). • Modelling HMM variants as DBNs. • …