ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1904.12157
  4. Cited By
Optimal Scaling of Random-Walk Metropolis Algorithms on General Target
  Distributions

Optimal Scaling of Random-Walk Metropolis Algorithms on General Target Distributions

27 April 2019
Jun Yang
Gareth O. Roberts
Jeffrey S. Rosenthal
    OT
ArXivPDFHTML

Papers citing "Optimal Scaling of Random-Walk Metropolis Algorithms on General Target Distributions"

14 / 14 papers shown
Title
New affine invariant ensemble samplers and their dimensional scaling
New affine invariant ensemble samplers and their dimensional scaling
Yifan Chen
81
0
0
05 May 2025
Coupling and Convergence for Hamiltonian Monte Carlo
Coupling and Convergence for Hamiltonian Monte Carlo
Nawaf Bou-Rabee
A. Eberle
Raphael Zimmer
88
137
0
01 May 2018
Dimension-Robust MCMC in Bayesian Inverse Problems
Dimension-Robust MCMC in Bayesian Inverse Problems
Victor Chen
Matthew M. Dunlop
O. Papaspiliopoulos
Andrew M. Stuart
47
36
0
09 Mar 2018
Log-concave sampling: Metropolis-Hastings algorithms are fast
Log-concave sampling: Metropolis-Hastings algorithms are fast
Raaz Dwivedi
Yuansi Chen
Martin J. Wainwright
Bin Yu
66
253
0
08 Jan 2018
User-friendly guarantees for the Langevin Monte Carlo with inaccurate
  gradient
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. Dalalyan
Avetik G. Karagulyan
65
296
0
29 Sep 2017
A Dirichlet Form approach to MCMC Optimal Scaling
A Dirichlet Form approach to MCMC Optimal Scaling
Giacomo Zanella
W. Kendall
M. Bédard
33
9
0
05 Jun 2016
Optimal scaling of the Random Walk Metropolis algorithm under Lp mean
  differentiability
Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability
Alain Durmus
Sylvain Le Corff
Eric Moulines
Gareth O. Roberts
OT
26
10
0
22 Apr 2016
MCMC-Based Inference in the Era of Big Data: A Fundamental Analysis of
  the Convergence Complexity of High-Dimensional Chains
MCMC-Based Inference in the Era of Big Data: A Fundamental Analysis of the Convergence Complexity of High-Dimensional Chains
B. Rajaratnam
Doug Sparks
92
65
0
05 Aug 2015
Theoretical guarantees for approximate sampling from smooth and
  log-concave densities
Theoretical guarantees for approximate sampling from smooth and log-concave densities
A. Dalalyan
66
514
0
23 Dec 2014
On the efficiency of pseudo-marginal random walk Metropolis algorithms
On the efficiency of pseudo-marginal random walk Metropolis algorithms
Chris Sherlock
Alexandre Hoang Thiery
Gareth O. Roberts
Jeffrey S. Rosenthal
55
190
0
27 Sep 2013
MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster
MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster
S. Cotter
Gareth O. Roberts
Andrew M. Stuart
D. White
91
480
0
03 Feb 2012
Optimal scaling and diffusion limits for the Langevin algorithm in high
  dimensions
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
Natesh S. Pillai
Andrew M. Stuart
Alexandre Hoang Thiery
87
99
0
02 Mar 2011
Diffusion limits of the random walk Metropolis algorithm in high
  dimensions
Diffusion limits of the random walk Metropolis algorithm in high dimensions
Jonathan C. Mattingly
Natesh S. Pillai
Andrew M. Stuart
92
114
0
22 Mar 2010
Optimal scaling of the random walk Metropolis on elliptically symmetric
  unimodal targets
Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
Chris Sherlock
Gareth O. Roberts
72
61
0
04 Sep 2009
1