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Monte Carlo Approximation of Bayes Factors via Mixing With Surrogate Distributions

By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang–Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the multiple-try Metropolis (MTM); (ii) constructing the surrogate via the variational approximation. When a surrogate is difficult to come by, we describe a new jumping mechanism for general reversible jump Markov chain Monte Carlo algorithms, which combines the MTM and a directional sampling algorithm. We illustrate the proposed methods on several statistical models, including the log-Gaussian Cox process, the Bayesian Lasso, the logistic regression, and the g-prior Bayesian variable selection. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.538 June 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 765-780

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

REVERSIBLE JUMP MCMC
MARGINAL LIKELIHOOD
MIXTURE DISTRIBUTION
MULTIPLE-TRY METROPOLIS
WANG–LANDAU ALGORITHM

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1811100

Pernyataan Tanggungjawab

Chenguang Dai, Jun S. Liu

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