Title | ||
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Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs |
Abstract | ||
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This paper presents a new sampling algorithm for approximating func- tions of variables representable as undirected graphical models of arbi- trary connectivity with pairwise potentials, as well as for estimating the notoriously dif(cid:2)cult partition function of the graph. The algorithm (cid:2)ts into the framework of sequential Monte Carlo methods rather than the more widely used MCMC, and relies on constructing a sequence of in- termediate distributions which get closer to the desired one. While the idea of using (cid:147)tempered(cid:148) proposals is known, we construct a novel se- quence of target distributions where, rather than dropping a global tem- perature parameter, we sequentially couple individual pairs of variables that are, initially, sampled exactly from a spanning tree of the variables. We present experimental results on inference and estimation of the parti- tion function for sparse and densely-connected graphs. |
Year | Venue | Field |
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2005 | NIPS | Pairwise comparison,Normalization (statistics),Markov chain Monte Carlo,Partition function (statistical mechanics),Inference,Computer science,Artificial intelligence,Spanning tree,Sampling (statistics),Graphical model,Machine learning |
DocType | Citations | PageRank |
Conference | 4 | 0.51 |
References | Authors | |
3 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Firas Hamze | 1 | 131 | 14.05 |
Nando De Freitas | 2 | 3284 | 273.68 |