Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors

16 March 2021

Papers citing "Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors"

7 / 7 papers shown

Title
A Proximal Newton Adaptive Importance Sampler Victor Elvira Émilie Chouzenoux O. Deniz Akyildiz 171 0 0 21 Dec 2024
On MCMC mixing under unidentified nonparametric models with an application to survival predictions under transformation models Chong Zhong Jin Yang Junshan Shen Catherine C. Liu Zhaohai Li 78 0 0 03 Nov 2024
Proximal Interacting Particle Langevin Algorithms Paula Cordero Encinar F. R. Crucinio O. Deniz Akyildiz 104 5 0 20 Jun 2024
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms Tim Tsz-Kit Lau Han Liu Thomas Pock 88 4 0 25 May 2023
Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes Tim Tsz-Kit Lau Han Liu 122 7 0 10 Jul 2022
Efficient Bayesian computation for low-photon imaging problems Savvas Melidonis P. Dobson Y. Altmann Marcelo Pereyra K. Zygalakis 44 12 0 10 Jun 2022
Reversible Jump PDMP Samplers for Variable Selection Augustin Chevallier Paul Fearnhead Matthew Sutton 57 18 0 22 Oct 2020