Text

Population Quasi-Monte Carlo

Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an important class of Monte Carlo methods, which adapts a population of proposals to generate weighted samples that approximate the target distribution. When the target distribution is expensive to evaluate, PMC may encounter computational limitations since it requires many evaluations of the target distribution. To address this, we propose a new method, Population Quasi-Monte Carlo (PQMC), which integrates Quasi-Monte Carlo ideas within the sampling and adaptation steps of PMC. A key novelty in PQMC is the idea of importance support points resampling, a deterministic method for finding an “optimal” subsample from the weighted proposal samples. Moreover, within the PQMC framework, we develop an efficient covariance adaptation strategy for multivariate normal proposals. Finally, a new set of correction weights is introduced for the weighted PMC estimator to improve the efficiency from the standard PMC estimator. We demonstrate the improved empirical performance of PQMC over PMC in extensive numerical simulations and a friction drilling application. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.31 No.3 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 695-708

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MONTE CARLO
IMPORTANCE SAMPLING
RESAMPLING
QUASI-MONTE CARLO
BAYESIAN COMPUTATION
SUPPORT POINTS

Info Detail Spesifik

https://doi.org/10.1080/10618600.2022.2034637

Pernyataan Tanggungjawab

Chaofan Huang, V. Roshan Joseph, Simon Mak

Versi lain/terkait

Lampiran Berkas

Komentar