Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)
Reimar Hofmann, Volker Tresp
We address the problem oflearning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidi(cid:173) mensional density estimation exploiting certain conditional independencies in the variables. Markov networks are a graphical way of describing con(cid:173) ditional independencies well suited to model relationships which do not ex(cid:173) hibit a natural causal ordering. We use neural network structures to model the quantitative relationships between variables. The main focus in this pa(cid:173) per will be on learning the structure for the purpose of gaining insight into the underlying process. Using two data sets we show that interesting struc(cid:173) tures can be found using our approach. Inference will be briefly addressed.