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Abstract: The application of latent/hidden variable Dynamic Bayesian Networks is constrained by the complexity of marginalising over latent variables. For this reason either small latent dimensions or Gaussian 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 specialisation has the advantage... (Update)
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BibTeX entry: (Update)
D. Barber, Dynamic Bayesian Networks with Deterministic Latent Tables, Neural Information Processing Systems (2003). http://citeseer.ist.psu.edu/barber03dynamic.html More
@misc{ barber03dynamic,
author = "D. Barber",
title = "Dynamic Bayesian Networks with Deterministic Latent Tables",
text = "D. Barber, Dynamic Bayesian Networks with Deterministic Latent Tables,
Neural Information Processing Systems (2003).",
year = "2003",
url = "citeseer.ist.psu.edu/barber03dynamic.html" }
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