** Next:** Introduction

# Exploiting Causal Independence in Bayesian Network Inference

** Nevin Lianwen Zhang **

lzhang@cs.ust.hk

Department of Computer Science,

University of Science & Technology,
Hong Kong

David Poole

poole@cs.ubc.ca

Department of Computer Science, University of British Columbia,

2366 Main Mall,
Vancouver, B.C., Canada V6T 1Z4

### Abstract:

*A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as
representing a factorization of a joint probability into the
multiplication of a set of conditional probabilities. We present a
notion of causal independence that enables one to further factorize
the conditional probabilities into a combination of even smaller
factors and consequently obtain a finer-grain factorization of the
joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence
and a query variable, uses the factorization to find the posterior
distribution of the query. We show how this algorithm can be extended
to exploit causal independence. Empirical studies, based on the CPCS
networks for medical diagnosis, show that this method is
more efficient than previous methods and allows for inference in
larger networks than previous algorithms.
*

*David Poole *

Fri Dec 6 15:09:32 PST 1996