*** The following NIPS*97 preprint is available ***
Nonlinear Markov Networks for Continuous Variables
Reimar Hofmann and Volker Tresp
SIEMENS AG, Corporate Technology
Abstract
In this paper we address the problem of learning the structure
in nonlinear Markov networks with continuous variables. Markov
networks are well suited to model relationships which do not
exhibit a natural causal ordering. We use neural network structures
to model the quantitative relationships between variables. Using a
financial and a social data set we show that interesting structures
can be found using our approach.
Available by ftp from:
ftp://flop.informatik.tu-muenchen.de/pub/hofmannr/nips97prerl.ps.gz
or from my homepage
http://wwwbrauer.informatik.tu-muenchen.de/~hofmannr
Also of interest might be our NIPS*95 paper which addresses the
corresponding problem for Bayesian networks:
Discovering Structure in Continuous Variables Using Bayesian Networks
Reimar Hofmann and Volker Tresp
SIEMENS AG, Corporate Technology
Abstract
We study Bayesian networks for continuous variables using nonlinear
conditional density estimators. We demonstrate that useful structures
can be extracted from a data set in a self-organized way and we present
sampling techniques for belief update based on Markov blanket
conditional density models.