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Adaptive Multiple Importance Sampling

Abstract : The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of the deterministic multiple mixture estimator of Owen and Zhou (2000). Although the convergence properties of the algorithm cannot be fully investigated, we demonstrate through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial.
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Contributor : Christian Robert Connect in order to contact the contributor
Submitted on : Thursday, July 9, 2009 - 5:42:48 PM
Last modification on : Tuesday, January 18, 2022 - 3:24:32 PM

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Jean-Marie Cornuet, Jean-Michel Marin, Antonietta Mira, Christian Robert. Adaptive Multiple Importance Sampling. Scandinavian Journal of Statistics, Wiley, 2012, 39 (4), pp.798-812. ⟨10.1111/j.1467-9469.2011.00756.x⟩. ⟨hal-00403248⟩



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