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Random Construction of Interpolating Sets for High-Dimensional Integration

Part of: Special processes Probabilistic theory: distribution modulo $1$; metric theory of algorithms Numerical approximation and computational geometry

Published online by Cambridge University Press:  30 January 2018

Mark Huber  and
Sarah Schott
Mark Huber*
Affiliation:
Claremont McKenna College
Sarah Schott*
Affiliation:
Duke University
*
∗ Postal address: Department of Mathematical Sciences, Claremont McKenna College, 850 Columbia Avenue, Claremont, CA 91711, USA. Email address: mhuber@cmc.edu
∗∗ Postal address: Department of Mathematics, Duke University, 117 Physics Building, Science Drive, Durham, NC 27708, USA. Email address: schott@math.duke.edu
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Abstract

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Computing the value of a high-dimensional integral can often be reduced to the problem of finding the ratio between the measures of two sets. Monte Carlo methods are often used to approximate this ratio, but often one set will be exponentially larger than the other, which leads to an exponentially large variance. A standard method of dealing with this problem is to interpolate between the sets with a sequence of nested sets where neighboring sets have relative measures bounded above by a constant. Choosing such a well-balanced sequence can rarely be done without extensive study of a problem. Here a new approach that automatically obtains such sets is presented. These well-balanced sets allow for faster approximation algorithms for integrals and sums using fewer samples, and better tempering and annealing Markov chains for generating random samples. Applications, such as finding the partition function of the Ising model and normalizing constants for posterior distributions in Bayesian methods, are discussed.

Keywords

IntegrationMonte Carlo methodcooling scheduleself-reducible

MSC classification

Primary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory
Secondary: 11K45: Pseudo-random numbers; Monte Carlo methods 65D30: Numerical integration

Information

Type
Research Article
Information
Journal of Applied Probability , Volume 51 , Issue 1 , March 2014 , pp. 92 - 105
Copyright
© Applied Probability Trust 

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