Simple Monte Carlo and the Metropolis algorithm |
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Authors: | Peter Math ,Erich Novak |
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Affiliation: | aWeierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, D-10117 Berlin, Germany;bFriedrich Schiller University Jena, Mathem. Institute, Ernst-Abbe-Platz 2, D-07743 Jena, Germany |
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Abstract: | We study the integration of functions with respect to an unknown density. Information is available as oracle calls to the integrand and to the non-normalized density function. We are interested in analyzing the integration error of optimal algorithms (or the complexity of the problem) with emphasis on the variability of the weight function. For a corresponding large class of problem instances we show that the complexity grows linearly in the variability, and the simple Monte Carlo method provides an almost optimal algorithm. Under additional geometric restrictions (mainly log-concavity) for the density functions, we establish that a suitable adaptive local Metropolis algorithm is almost optimal and outperforms any non-adaptive algorithm. |
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Keywords: | Monte Carlo methods Metropolis algorithm Log-concave density Rapidly mixing Markov chains Optimal algorithms Adaptivity Complexity |
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