Notes on Cutset Conditioning on Factor Graphs with Cycles
نویسنده
چکیده
Inference on factor graphs with loops with the standard forwardbackward algorithm, can give unpredictible results as messages can travel indefinitely in the system with no guarantee on convergence. We apply the exact method of cutset conditioning to Factor Graphs with loops starting from a fully developed three-variable example and providing comments and suggestions for distributed implementations.
منابع مشابه
Optimization of Pearl's Method of Conditioning and Greedy-Like Approximation Algorithms for the Vertex Feedback Set Problem
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first :itep in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. The algorithm is based on a reduction to the ...
متن کاملA combination of cutset conditioning with clique-tree propagation in the Pathfinder system
Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We describe a means to combine cutset conditioning and clique- tree propagation in an approach called aggregat...
متن کاملOn Stable Cutsets in Claw-Free Graphs and Planar Graphs
A stable cutset in a connected graph is a stable set whose deletion disconnects the graph. Let K4 and K1,3 (claw) denote the complete (bipartite) graph on 4 and 1+ 3 vertices. It is NP-complete to decide whether a line graph (hence a claw-free graph) with maximum degree five or a K4-free graph admits a stable cutset. Here we describe algorithms deciding in polynomial time whether a claw-free gr...
متن کاملA combination of exact algorithms for inference on Bayesian belief networks
Cutset conditioning and clique-tree propagation are two popular methods for exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We characterize network structures in which the performances of these methods differ. We describe a means to combine cuts...
متن کاملAn Anytime Scheme for Bounding Posterior Beliefs
This paper presents an any-time scheme for computing lower and upper bounds on posterior marginals in Bayesian networks. The scheme draws from two previously proposed methods, bounded conditioning (Horvitz, Suermondt, & Cooper 1989) and bound propagation (Leisink & Kappen 2003). Following the principles of cutset conditioning (Pearl 1988), our method enumerates a subset of cutset tuples and app...
متن کامل