Abstract of FKI-235-00
Title: Inference in Markov Blanket Networks
Authors: Reimar Hofmann
Category: Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract: Bayesian networks have been successfully used to model
joint probabilities in many cases.
When dealing with continuous variables and nonlinear
relationships neural networks can be used to model conditional
densities as part of a Bayesian network.
However, doing inference can then be computationally
Also, information is implicitly passed backwards through
neural networks, i.e. from their output to the input.
Used in this ``inverse'' mode neural networks often perform
We suggest a different type of model called Markov
blanket model (MBM). Here the neural networks are used in
the forward direction only. This gives advantages in speed
and guarantees to match the performance of the underlying
neural network on complete data.
Keywords: Gibbs sampling, Neural networks, Bayesian networks,
Markov networks, Graphical models, Markov blanket
models, Gibbs sampling from inconsistent distributions.
Size: 8 pages
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