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    On the use of stochastic approximation Monte Carlo for Monte Carlo integration

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    Type
    Article
    Authors
    Liang, Faming
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2009-03
    Permanent link to this record
    http://hdl.handle.net/10754/599069
    
    Metadata
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    Abstract
    The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration via a dynamically weighted estimator by calling some results from the literature of nonhomogeneous Markov chains. Our numerical results indicate that SAMC can yield significant savings over conventional Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, for the problems for which the energy landscape is rugged. © 2008 Elsevier B.V. All rights reserved.
    Citation
    Liang F (2009) On the use of stochastic approximation Monte Carlo for Monte Carlo integration. Statistics & Probability Letters 79: 581–587. Available: http://dx.doi.org/10.1016/j.spl.2008.10.007.
    Sponsors
    The author's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The author thanks Professors Chuanhai Liu and Minghui Chen for their early discussions on the topic, and thanks Professor Hira Koul, the associate editor, and the referee for their comments which have led to significant improvement of this paper.
    Publisher
    Elsevier BV
    Journal
    Statistics & Probability Letters
    DOI
    10.1016/j.spl.2008.10.007
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.spl.2008.10.007
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