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Adaptive multiple importance sampling for Gaussian processes

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    0469804 - ÚTIA 2018 RIV GB eng J - Článek v odborném periodiku
    Xiong, X. - Šmídl, Václav - Filippone, M.
    Adaptive multiple importance sampling for Gaussian processes.
    Journal of Statistical Computation and Simulation. Roč. 87, č. 8 (2017), s. 1644-1665. ISSN 0094-9655
    Grant CEP: GA MŠk(CZ) 7F14287
    Institucionální podpora: RVO:67985556
    Klíčová slova: Gaussian Process * Bayesian estimation * Adaptive importance sampling
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    Obor OECD: Statistics and probability
    Impakt faktor: 0.869, rok: 2017

    In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.
    Trvalý link: http://hdl.handle.net/11104/0270731
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