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Spectral gaps and error estimates for infinite-dimensional
  Metropolis-Hastings with non-Gaussian priors
v1v2v3 (latest)

Spectral gaps and error estimates for infinite-dimensional Metropolis-Hastings with non-Gaussian priors

30 September 2018
Bamdad Hosseini
J. Johndrow
ArXiv (abs)PDFHTML

Papers citing "Spectral gaps and error estimates for infinite-dimensional Metropolis-Hastings with non-Gaussian priors"

13 / 13 papers shown
Title
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning
  Algorithms
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
Matthew M. Dunlop
D. Slepčev
Andrew M. Stuart
Matthew Thorpe
45
61
0
23 May 2018
Two Metropolis-Hastings algorithms for posterior measures with
  non-Gaussian priors in infinite dimensions
Two Metropolis-Hastings algorithms for posterior measures with non-Gaussian priors in infinite dimensions
Bamdad Hosseini
51
13
0
20 Apr 2018
Well-posed Bayesian Inverse Problems with Infinitely-Divisible and
  Heavy-Tailed Prior Measures
Well-posed Bayesian Inverse Problems with Infinitely-Divisible and Heavy-Tailed Prior Measures
Bamdad Hosseini
75
34
0
23 Sep 2016
Well-posed Bayesian inverse problems and heavy-tailed stable
  quasi-Banach space priors
Well-posed Bayesian inverse problems and heavy-tailed stable quasi-Banach space priors
T. Sullivan
56
41
0
19 May 2016
Well-posed Bayesian Inverse Problems: Priors with Exponential Tails
Well-posed Bayesian Inverse Problems: Priors with Exponential Tails
Bamdad Hosseini
N. Nigam
58
46
0
09 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
127
66
0
05 Aug 2015
Perturbation theory for Markov chains via Wasserstein distance
Perturbation theory for Markov chains via Wasserstein distance
Daniel Rudolf
Nikolaus Schweizer
87
108
0
13 Mar 2015
Ergodicity of Approximate MCMC Chains with Applications to Large Data
  Sets
Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets
Natesh S. Pillai
Aaron Smith
67
59
0
01 May 2014
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
102
480
0
03 Feb 2012
Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
Martin Hairer
Andrew M. Stuart
Sebastian J. Vollmer
100
188
0
06 Dec 2011
Besov priors for Bayesian inverse problems
Besov priors for Bayesian inverse problems
Masoumeh Dashti
Stephen Harris
Andrew M. Stuart
89
119
0
04 May 2011
Uncertainty quantification and weak approximation of an elliptic inverse
  problem
Uncertainty quantification and weak approximation of an elliptic inverse problem
Masoumeh Dashti
Andrew M. Stuart
66
102
0
01 Feb 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
113
114
0
22 Mar 2010
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