Annealed importance sampling
 Radford M. Neal
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Simulated annealing—moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions—has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspect allows this method to perform acceptably even for highdimensional problems, where finding good importance sampling distributions would otherwise be very difficult, while the use of importance weights ensures that the estimates found converge to the correct values as the number of annealing runs increases. This annealed importance sampling procedure resembles the second half of the previouslystudied tempered transitions, and can be seen as a generalization of a recentlyproposed variant of sequential importance sampling. It is also related to thermodynamic integration methods for estimating ratios of normalizing constants. Annealed importance sampling is most attractive when isolated modes are present, or when estimates of normalizing constants are required, but it may also be more generally useful, since its independent sampling allows one to bypass some of the problems of assessing convergence and autocorrelation in Markov chain samplers.
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 Title
 Annealed importance sampling
 Journal

Statistics and Computing
Volume 11, Issue 2 , pp 125139
 Cover Date
 20010401
 DOI
 10.1023/A:1008923215028
 Print ISSN
 09603174
 Online ISSN
 15731375
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 tempered transitions
 sequential importance sampling
 estimation of normalizing constants
 free energy computation
 Industry Sectors
 Authors

 Radford M. Neal ^{(1)}
 Author Affiliations

 1. Department of Statistics and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada