Context-specific independence in Bayesian networks (1996)by C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller
Abstract:
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme-tree-structured CPTs-for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms. In particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on cutset conditioning.
Download Information
|
C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller (1996). "Context-specific independence in Bayesian networks." Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI '96) (pp. 115-123).
|
|
Bibtex citation
@inproceedings{Boutilier+al:UAI96,
author = "C. Boutilier and N. Friedman and M. Goldszmidt
and D. Koller",
booktitle = "Proceedings of the Twelfth Annual Conference on
Uncertainty in Artificial Intelligence (UAI '96)",
title = "Context-specific independence in {B}ayesian networks",
publisher = "Morgan Kaufman",
pages = "115--123",
month = aug,
year = "1996",
city = "Portland, Oregon",
}
full list
|