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Scalable Metropolis-Hastings for Exact Bayesian Inference with Large
  Datasets

Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets

28 January 2019
R. Cornish
Paul Vanetti
Alexandre Bouchard-Coté
George Deligiannidis
Arnaud Doucet
ArXivPDFHTML

Papers citing "Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets"

5 / 5 papers shown
Title
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Conor Rosato
Harvinder Lehal
Simon Maskell
L. Devlin
Malcolm Strens
19
0
0
15 May 2025
An invitation to sequential Monte Carlo samplers
An invitation to sequential Monte Carlo samplers
Chenguang Dai
J. Heng
Pierre E. Jacob
N. Whiteley
52
65
0
23 Jul 2020
Efficient MCMC Sampling with Dimension-Free Convergence Rate using
  ADMM-type Splitting
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono
Daniel Paulin
Arnaud Doucet
24
37
0
23 May 2019
Quasi-stationary Monte Carlo and the ScaLE Algorithm
Quasi-stationary Monte Carlo and the ScaLE Algorithm
M. Pollock
Paul Fearnhead
A. M. Johansen
Gareth O. Roberts
31
18
0
12 Sep 2016
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis
  of Big Data
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
J. Bierkens
Paul Fearnhead
Gareth O. Roberts
58
231
0
11 Jul 2016
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