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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Vydané v:	Journal of the Royal Statistical Society. Series B, Statistical methodology Ročník 81; číslo 3; s. 489 - 517
Hlavní autori:	Zanella, Giacomo, Roberts, Gareth
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Wiley 01.07.2019 Oxford University Press
Predmet:	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
On-line prístup:	Získať plný text
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