English

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)

R2 v1 2026-06-21T23:33:42.610Z