A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference

We propose a very fast approximate Markov chain Monte Carlo sampling framework that is applicable to a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), where n is the sample size, s is the underlying sparsity...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2024-02, Vol.85 (5), p.1492-1516
Hauptverfasser:	Atchadé, Yves, Wang, Liwei
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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