Dynamic Bayesian Networks with Deterministic Latent Tables

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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David Barber


The application of latent/hidden variable Dynamic Bayesian Net- works is constrained by the complexity of marginalising over latent variables. For this reason either small latent dimensions or Gaus- sian latent conditional tables linearly dependent on past states are typically considered in order that inference is tractable. We suggest an alternative approach in which the latent variables are modelled using deterministic conditional probability tables. This specialisa- tion has the advantage of tractable inference even for highly com- plex non-linear/non-Gaussian visible conditional probability tables. This approach enables the consideration of highly complex latent dynamics whilst retaining the bene(cid:12)ts of a tractable probabilistic model.