Adaptive Multiple Importance Sampling

Robert, Christian P.; Mira, Antonietta; Marin, Jean-Michel; Cornuet, Jean-Marie

Robert, Christian P.; Mira, Antonietta; Marin, Jean-Michel; Cornuet, Jean-Marie (2012), Adaptive Multiple Importance Sampling, Scandinavian Journal of Statistics, 39, 4, p. 798-812. http://dx.doi.org/10.1111/j.1467-9469.2011.00756.x

Type

Article accepté pour publication ou publié

Date

2012

Journal name

Scandinavian Journal of Statistics

Volume

Number

Publisher

Blackwell

Pages

798-812

Abstract (EN)

. The Adaptive Multiple Importance Sampling algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling (IS) scheme. The difference with earlier adaptive IS 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 & Zhou ( J. Amer. Statist. Assoc., 95, 2000, 135). Although the convergence properties of the algorithm cannot be investigated, we demonstrate through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial. [ABSTRACT FROM AUTHOR]

Subjects / Keywords

sequential Monte Carlo; population Monte Carlo; population genetics; particle filters; deterministic mixture weights; banana shape target

JEL

C15 - Statistical Simulation Methods: General