@inproceedings{milch2005approximate,
author = {Milch, Brian and Marthi, Bhaskara and Sontag, David and Russell, Stuart and Ong, Daniel L. and Kolobov, Andrey},
title = {Approximate Inference for Infinite Contingent Bayesian Networks},
booktitle = {AISTATS 2005},
year = {2005},
month = {January},
abstract = {In many practical problems — from tracking aircraft based on radar data to building a bibliographic database based on citation lists — we want to reason about an unbounded number of unseen objects with unknown relations among them. Bayesian networks, which define a fixed dependency structure on a finite set of variables, are not the ideal representation language for this task. This paper introduces contingent Bayesian networks (CBNs), which represent uncertainty about dependencies by labeling each edge with a condition under which it is active. A CBN may contain cycles and have infinitely many variables. Nevertheless, we give general conditions under which such a CBN defines a unique joint distribution over its variables. We also present a likelihood weighting algorithm that performs approximate inference in finite time per sampling step on any CBN that satisfies these conditions.},
url = {http://approjects.co.za/?big=en-us/research/publication/approximate-inference-infinite-contingent-bayesian-networks/},
}