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Particle Metropolis-Hastings using gradient and Hessian information
v1v2v3v4 (latest)

Particle Metropolis-Hastings using gradient and Hessian information

Statistics and computing (Stat. Comput.), 2013
4 November 2013
J. Dahlin
Fredrik Lindsten
Thomas B. Schon
ArXiv (abs)PDFHTML

Papers citing "Particle Metropolis-Hastings using gradient and Hessian information"

22 / 22 papers shown
Sequential Monte Carlo with active subspaces
Sequential Monte Carlo with active subspaces
Leonardo Ripoli
Richard G. Everitt
160
2
0
08 Nov 2024
Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable
  Particle Filters Within Langevin Proposals
Enhanced SMC2^22: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Conor Rosato
Joshua Murphy
Alessandro Varsi
P. Horridge
Simon Maskell
356
8
0
24 Jul 2024
Efficient Learning of the Parameters of Non-Linear Models using
  Differentiable Resampling in Particle Filters
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle FiltersIEEE Transactions on Signal Processing (IEEE TSP), 2021
Conor Rosato
Vincent Beraud
P. Horridge
Thomas B. Schon
Simon Maskell
378
25
0
02 Nov 2021
Nonlinear input design as optimal control of a Hamiltonian system
Nonlinear input design as optimal control of a Hamiltonian systemIEEE Control Systems Letters (L-CSS), 2019
Jack Umenberger
Thomas B. Schon
155
5
0
06 Mar 2019
Stochastic Gradient MCMC for Nonlinear State Space Models
Stochastic Gradient MCMC for Nonlinear State Space ModelsBayesian Analysis (BA), 2019
Christopher Aicher
Srshti Putcha
Christopher Nemeth
Paul Fearnhead
E. Fox
BDL
501
8
0
29 Jan 2019
Regularized Zero-Variance Control Variates
Regularized Zero-Variance Control Variates
Leah F. South
Chris J. Oates
Antonietta Mira
Christopher C. Drovandi
BDL
763
23
0
13 Nov 2018
Simulation-based inference methods for partially observed Markov model
  via the R package is2
Simulation-based inference methods for partially observed Markov model via the R package is2
Bernhard Bergmair
Johann Hoffelner
Siegfried Silber
374
0
0
07 Nov 2018
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton
  proposals
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
J. Dahlin
A. Wills
B. Ninness
209
0
0
26 Jun 2018
Constructing Metropolis-Hastings proposals using damped BFGS updates
Constructing Metropolis-Hastings proposals using damped BFGS updates
J. Dahlin
A. Wills
B. Ninness
227
2
0
04 Jan 2018
Particle Filters and Data Assimilation
Particle Filters and Data Assimilation
Paul Fearnhead
H. Kunsch
230
91
0
13 Sep 2017
Fast approximate Bayesian inference for stable differential equation
  models
Fast approximate Bayesian inference for stable differential equation models
Philip Maybank
I. Bojak
R. Everitt
348
10
0
02 Jun 2017
Probabilistic learning of nonlinear dynamical systems using sequential
  Monte Carlo
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Thomas B. Schon
Andreas Svensson
Lawrence M. Murray
Fredrik Lindsten
278
42
0
07 Mar 2017
A rare event approach to high dimensional Approximate Bayesian
  computation
A rare event approach to high dimensional Approximate Bayesian computation
D. Prangle
R. Everitt
T. Kypraios
360
23
0
08 Nov 2016
A Common Derivation for Markov Chain Monte Carlo Algorithms with
  Tractable and Intractable Targets
A Common Derivation for Markov Chain Monte Carlo Algorithms with Tractable and Intractable Targets
K. Tran
BDL
380
2
0
07 Jul 2016
Accelerating pseudo-marginal Metropolis-Hastings by correlating
  auxiliary variables
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
J. Dahlin
Fredrik Lindsten
J. Kronander
Thomas B. Schon
194
37
0
17 Nov 2015
Getting Started with Particle Metropolis-Hastings for Inference in
  Nonlinear Dynamical Models
Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
J. Dahlin
Thomas B. Schon
652
27
0
05 Nov 2015
Sequential Monte Carlo Methods for System Identification
Sequential Monte Carlo Methods for System Identification
Thomas B. Schon
Fredrik Lindsten
J. Dahlin
Johan Waagberg
C. A. Naesseth
Andreas Svensson
L. Dai
345
82
0
20 Mar 2015
Quasi-Newton particle Metropolis-Hastings
Quasi-Newton particle Metropolis-Hastings
J. Dahlin
Fredrik Lindsten
Thomas B. Schon
395
9
0
12 Feb 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
520
469
0
30 Dec 2014
Particle Metropolis-adjusted Langevin algorithms
Particle Metropolis-adjusted Langevin algorithms
Christopher Nemeth
Chris Sherlock
Paul Fearnhead
462
25
0
23 Dec 2014
Augmentation Schemes for Particle MCMC
Augmentation Schemes for Particle MCMCStatistics and computing (Stat Comput), 2014
Paul Fearnhead
Loukia Meligkotsidou
278
22
0
29 Aug 2014
Data augmentation for models based on rejection sampling
Data augmentation for models based on rejection sampling
Vinayak A. Rao
Lizhen Lin
David B. Dunson
BDL
350
27
0
25 Jun 2014
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