Inter-causal Independence and Heterogeneous Factorization
Artificial Intelligence
2013-02-28 v1
Abstract
It is well known that conditional independence can be used to factorize a joint probability into a multiplication of conditional probabilities. This paper proposes a constructive definition of inter-causal independence, which can be used to further factorize a conditional probability. An inference algorithm is developed, which makes use of both conditional independence and inter-causal independence to reduce inference complexity in Bayesian networks.
Keywords
Cite
@article{arxiv.1302.6855,
title = {Inter-causal Independence and Heterogeneous Factorization},
author = {Nevin Lianwen Zhang and David L Poole},
journal= {arXiv preprint arXiv:1302.6855},
year = {2013}
}
Comments
Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)