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An Introduction to Twisted Particle Filters and Parameter Estimation in
  Non-linear State-space Models
v1v2 (latest)

An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models

30 September 2015
Juha Ala-Luhtala
N. Whiteley
K. Heine
R. Piché
ArXiv (abs)PDFHTML

Papers citing "An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models"

4 / 4 papers shown
Title
Latent Parameter Estimation in Fusion Networks Using Separable
  Likelihoods
Latent Parameter Estimation in Fusion Networks Using Separable Likelihoods
Murat Üney
B. Mulgrew
Daniel E. Clark
FedML
49
14
0
02 Aug 2017
Analysis of a nonlinear importance sampling scheme for Bayesian
  parameter estimation in state-space models
Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models
Joaquín Míguez
I. P. Mariño
M. A. Vázquez
45
11
0
10 Feb 2017
Importance sampling type estimators based on approximate marginal MCMC
Importance sampling type estimators based on approximate marginal MCMC
M. Vihola
Jouni Helske
Jordan Franks
110
25
0
08 Sep 2016
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
97
25
0
05 Nov 2015
1