The Barker proposal: combining robustness and efficiency in
gradient-based MCMC

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The Barker proposal: combining robustness and efficiency in gradient-based MCMC

30 August 2019

Samuel Livingstone

Giacomo Zanella

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Papers citing "The Barker proposal: combining robustness and efficiency in gradient-based MCMC"

11 / 11 papers shown

Title
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy Xingtu Liu Andrew B. Duncan Axel Gandy 76 7 0 28 Apr 2023
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces Haoran Sun H. Dai Dale Schuurmans 90 13 0 16 Sep 2022
Discrete Langevin Sampler via Wasserstein Gradient Flow Haoran Sun H. Dai Bo Dai Haomin Zhou Dale Schuurmans BDL 90 24 0 29 Jun 2022
Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte Carlo L. Riou-Durand Jure Vogrinc 90 15 0 26 Feb 2022
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection Xitong Liang Samuel Livingstone Jim Griffin BDL 60 10 0 22 Oct 2021
A fresh take on 'Barker dynamics' for MCMC Max Hird Samuel Livingstone Giacomo Zanella 93 9 0 17 Dec 2020
Penalised t-walk MCMC F. Medina-Aguayo A. Christen 70 3 0 03 Dec 2020
Measure Transport with Kernel Stein Discrepancy Matthew A. Fisher T. Nolan Matthew M. Graham D. Prangle Chris J. Oates OT 124 15 0 22 Oct 2020
An invitation to sequential Monte Carlo samplers Chenguang Dai J. Heng Pierre E. Jacob N. Whiteley 132 68 0 23 Jul 2020
Optimal Thinning of MCMC Output M. Riabiz W. Chen Jon Cockayne P. Swietach Steven Niederer Lester W. Mackey Chris J. Oates 78 47 0 08 May 2020
Informed reversible jump algorithms Philippe Gagnon 35 12 0 05 Nov 2019