Scalable importance tempering and Bayesian variable selection

We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov...

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Bibliographic Details
Published in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 81; no. 3; pp. 489 - 517
Main Authors:	Zanella, Giacomo, Roberts, Gareth
Format:	Journal Article
Language:	English
Published:	Oxford Wiley 01.07.2019 Oxford University Press
Subjects:	Algorithms Bayesian analysis Bayesian theory Bayesian variable selection; Computational complexity; Gibbs sampling; Importance sampling; Markov chain Monte Carlo sampling; Point mass priors concrete equations Importance sampling Intuition Markov analysis Markov chain Markov chains Monte Carlo method Monte Carlo simulation probability distribution Regression analysis Robustness Sampling Statistical methods Statistics tempering
ISSN:	1369-7412, 1467-9868
Online Access:	Get full text
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