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1908.11812
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The Barker proposal: combining robustness and efficiency in gradient-based MCMC
Journal of The Royal Statistical Society Series B-statistical Methodology (JRSSB), 2019
30 August 2019
Samuel Livingstone
T. Rigon
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Papers citing
"The Barker proposal: combining robustness and efficiency in gradient-based MCMC"
33 / 33 papers shown
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Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
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Theoretical guarantees for lifted samplers
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Florian Maire
261
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Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm
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315
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Reinforcement Learning for Adaptive MCMC
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Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
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Giacomo Zanella
290
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Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
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Gareth O. Roberts
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autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
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Nikola Surjanovic
Saifuddin Syed
Trevor Campbell
Alexandre Bouchard-Côté
398
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Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
International Conference on Machine Learning (ICML), 2023
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Andrew B. Duncan
Axel Gandy
411
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Importance is Important: A Guide to Informed Importance Tempering Methods
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Aaron Smith
Quan Zhou
263
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Systematic approaches to generate reversiblizations of Markov chains
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Geoffrey Wolfer
367
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A Targeted Accuracy Diagnostic for Variational Approximations
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Yu Wang
Mikolaj Kasprzak
Jonathan H. Huggins
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178
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Improving multiple-try Metropolis with local balancing
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Philippe Gagnon
Florian Maire
T. Rigon
342
15
0
21 Nov 2022
Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles
The Annals of Applied Probability (Ann. Appl. Probab.), 2022
Christophe Andrieu
Anthony Lee
Samuel Power
Andi Q. Wang
222
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Sampling using Adaptive Regenerative Processes
Hector McKimm
Andi Q. Wang
M. Pollock
Christian P. Robert
Gareth O. Roberts
238
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Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Neural Information Processing Systems (NeurIPS), 2022
Haoran Sun
H. Dai
Dale Schuurmans
300
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Discrete Langevin Sampler via Wasserstein Gradient Flow
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Haoran Sun
H. Dai
Bo Dai
Haomin Zhou
Dale Schuurmans
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295
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0
29 Jun 2022
Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte Carlo
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Jure Vogrinc
384
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Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
Jure Vogrinc
Samuel Livingstone
T. Rigon
168
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04 Jan 2022
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection
Statistics and computing (Stat Comput), 2021
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Samuel Livingstone
Jim Griffin
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308
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Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions
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A. Barnett
Bob Carpenter
262
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Rapid Convergence of Informed Importance Tempering
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Quan Zhou
Aaron Smith
226
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0
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A fresh take on 'Barker dynamics' for MCMC
Monte Carlo and Quasi-Monte Carlo Methods (MCQMC), 2020
Max Hird
Samuel Livingstone
T. Rigon
349
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0
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Penalised t-walk MCMC
Journal of Statistical Planning and Inference (JSPI), 2020
F. Medina-Aguayo
A. Christen
283
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0
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Ultimate Pólya Gamma Samplers -- Efficient MCMC for possibly imbalanced binary and categorical data
Journal of the American Statistical Association (JASA), 2020
Gregor Zens
Sylvia Fruhwirth-Schnatter
Helga Wagner
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584
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0
13 Nov 2020
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher
T. Nolan
Matthew M. Graham
D. Prangle
Chris J. Oates
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338
15
0
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An invitation to sequential Monte Carlo samplers
Chenguang Dai
J. Heng
Pierre E. Jacob
N. Whiteley
473
83
0
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Optimal Thinning of MCMC Output
M. Riabiz
W. Chen
Jon Cockayne
P. Swietach
Steven Niederer
Lester W. Mackey
Chris J. Oates
574
57
0
08 May 2020
An asymptotic Peskun ordering and its application to lifted samplers
Bernoulli (Bernoulli), 2020
Philippe Gagnon
Florian Maire
415
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Informed reversible jump algorithms
Electronic Journal of Statistics (EJS), 2019
Philippe Gagnon
253
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