Abstract of FKI-235-00

Document-Name:  fki-235-00.ps.gz
Title:          Inference in Markov Blanket Networks
Authors:        Reimar Hofmann 
Revision-Date:  2000/02/10
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
Language:       English
ISSN:           0941-6358
Copyright:      The ``Forschungsberichte Künstliche Intelligenz''
                series includes primarily preliminary publications,
                specialized partial results, and supplementary
                material. In the interest of a subsequent final
                publication these reports should not be copied. All
                rights and the responsability for the contents of the
                report are with the authors, who would appreciate
                critical comments.

Matthias Nickles