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Post-Processing of MCMC
v1v2v3 (latest)

Post-Processing of MCMC

Annual Review of Statistics and Its Application (ARSIA), 2021
30 March 2021
Leah F. South
M. Riabiz
Onur Teymur
Chris J. Oates
ArXiv (abs)PDFHTML

Papers citing "Post-Processing of MCMC"

9 / 9 papers shown
Bayesian neural networks via MCMC: a Python-based tutorial
Bayesian neural networks via MCMC: a Python-based tutorialIEEE Access (IEEE Access), 2023
Rohitash Chandra
Royce Chen
Joshua Simmons
BDL
325
19
0
02 Apr 2023
Meta-learning Control Variates: Variance Reduction with Limited Data
Meta-learning Control Variates: Variance Reduction with Limited DataConference on Uncertainty in Artificial Intelligence (UAI), 2023
Z. Sun
Chris J. Oates
F. Briol
BDL
304
10
0
08 Mar 2023
Kernel Stein Discrepancy thinning: a theoretical perspective of
  pathologies and a practical fix with regularization
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularizationNeural Information Processing Systems (NeurIPS), 2023
Clément Bénard
B. Staber
Sébastien Da Veiga
329
9
0
31 Jan 2023
Multivariate strong invariance principles in Markov chain Monte Carlo
Multivariate strong invariance principles in Markov chain Monte CarloElectronic Journal of Statistics (EJS), 2022
Arka Banerjee
Dootika Vats
166
4
0
13 Nov 2022
A Fourier representation of kernel Stein discrepancy with application to
  Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spacesBernoulli (Bernoulli), 2022
George Wynne
Mikolaj Kasprzak
Andrew B. Duncan
301
4
0
09 Jun 2022
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous
  Bias-Variance Reduction and Supercanonical Convergence
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical ConvergenceJournal of machine learning research (JMLR), 2021
Henry Lam
Haofeng Zhang
314
4
0
23 Oct 2021
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and
  Depth in Functional Data Analysis
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data AnalysisInternational Statistical Review (ISR), 2021
George Wynne
Stanislav Nagy
235
6
0
26 May 2021
A Kernel Stein Test for Comparing Latent Variable Models
A Kernel Stein Test for Comparing Latent Variable Models
Heishiro Kanagawa
Wittawat Jitkrittum
Lester W. Mackey
Kenji Fukumizu
Arthur Gretton
435
19
0
01 Jul 2019
Variance reduction for additive functional of Markov chains via
  martingale representations
Variance reduction for additive functional of Markov chains via martingale representationsStatistics and computing (Stat. Comput.), 2019
Denis Belomestny
Eric Moulines
S. Samsonov
261
1
0
18 Mar 2019
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