Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation.

This article advocates proximal Markov chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of th...

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Detalles Bibliográficos

Publicado en:	American Statistician Vol. 78; no. 4; pp. 379 - 391
Autores principales:	Zhou, Xinkai, Heng, Qiang, Chi, Eric C., Zhou, Hua
Formato:	Artículo
Publicado:	Taylor & Francis Ltd Nov2024
Materias:	Markov chain Monte Carlo Bayesian field theory Inferential statistics Machine learning Parameter estimation Regularization parameter
Acceso en línea:	Ver este registro en EBSCOhost