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The divide-and-conquer sequential Monte Carlo algorithm: theoretical
  properties and limit theorems

The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems

29 October 2021
Juan Kuntz
F. R. Crucinio
A. M. Johansen
ArXivPDFHTML

Papers citing "The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems"

17 / 17 papers shown
Title
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Adrien Corenflos
Nicolas Chopin
Simo Särkkä
54
7
0
04 Feb 2022
Product-form estimators: exploiting independence to scale up Monte Carlo
Product-form estimators: exploiting independence to scale up Monte Carlo
Juan Kuntz
F. R. Crucinio
A. M. Johansen
80
11
0
23 Feb 2021
Design based incomplete U-statistics
Design based incomplete U-statistics
Xiangshun Kong
Wei Zheng
30
9
0
10 Aug 2020
Asymptotically exact data augmentation: models, properties and
  algorithms
Asymptotically exact data augmentation: models, properties and algorithms
Maxime Vono
N. Dobigeon
P. Chainais
50
27
0
15 Feb 2019
Global consensus Monte Carlo
Global consensus Monte Carlo
Lewis J. Rendell
A. M. Johansen
Anthony Lee
N. Whiteley
41
40
0
24 Jul 2018
Large Sample Asymptotics of the Pseudo-Marginal Method
Large Sample Asymptotics of the Pseudo-Marginal Method
Sebastian M. Schmon
George Deligiannidis
Arnaud Doucet
M. Pitt
51
31
0
26 Jun 2018
Tensor Monte Carlo: particle methods for the GPU era
Tensor Monte Carlo: particle methods for the GPU era
Laurence Aitchison
BDL
DRL
42
13
0
22 Jun 2018
Particle Filters and Data Assimilation
Particle Filters and Data Assimilation
Paul Fearnhead
H. Kunsch
53
81
0
13 Sep 2017
Negative association, ordering and convergence of resampling methods
Negative association, ordering and convergence of resampling methods
Mathieu Gerber
Nicolas Chopin
N. Whiteley
34
73
0
06 Jul 2017
Joining and splitting models with Markov melding
Joining and splitting models with Markov melding
Robert J. B. Goudie
A. Presanis
David J. Lunn
Daniela De Angelis
L. Wernisch
40
29
0
22 Jul 2016
Inference Networks for Sequential Monte Carlo in Graphical Models
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige
Frank Wood
BDL
135
110
0
22 Feb 2016
The iterated auxiliary particle filter
The iterated auxiliary particle filter
Pieralberto Guarniero
A. M. Johansen
Anthony Lee
OffRL
54
105
0
19 Nov 2015
Variance estimation in the particle filter
Variance estimation in the particle filter
Anthony Lee
N. Whiteley
28
43
0
01 Sep 2015
On Particle Methods for Parameter Estimation in State-Space Models
On Particle Methods for Parameter Estimation in State-Space Models
N. Kantas
Arnaud Doucet
Sumeetpal S. Singh
J. Maciejowski
Nicolas Chopin
61
433
0
30 Dec 2014
Divide-and-Conquer with Sequential Monte Carlo
Divide-and-Conquer with Sequential Monte Carlo
Fredrik Lindsten
A. M. Johansen
C. A. Naesseth
Bonnie Kirkpatrick
Thomas B. Schon
J. Aston
Alexandre Bouchard-Côté
66
45
0
19 Jun 2014
Asymptotically Exact, Embarrassingly Parallel MCMC
Asymptotically Exact, Embarrassingly Parallel MCMC
Willie Neiswanger
Chong-Jun Wang
Eric Xing
FedML
77
330
0
19 Nov 2013
The pseudo-marginal approach for efficient Monte Carlo computations
The pseudo-marginal approach for efficient Monte Carlo computations
Christophe Andrieu
Gareth O. Roberts
152
894
0
31 Mar 2009
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