Fetching the paper…
Reading the bibliography…
Sequential Monte Carlo (SMC) algorithms were originally designed for estimating intractable conditional expectations within state-space models, but are now routinely used to generate approximate samples in the context of general-purpose Bayesian inference.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J Gordon, David J Salmond, and Adrian FM Smith · 1993
Earlier work this paper cites.
On sequential Monte Carlo sampling methods for Bayesian filtering
Arnaud Doucet, Simon J Godsill, and Christophe Andrieu · 2000
Earlier work this paper cites.
Sequential Monte Carlo in Practice
Arnaud Doucet, Nando de Freitas, and Neil Gordon · 2001
Earlier work this paper cites.
Feynman-Kac Formulae: Genealogical and Interacting Particles Systems with Applications
Pierre Del Moral · 2004
Earlier work this paper cites.
Sequential Monte Carlo samplers
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra · 2006
Earlier work this paper cites.
Adaptive methods for sequential importance sampling with application to state space models
Julien Cornebise, E Moulines, and Jimmy Olsson · 2008
Earlier work this paper cites.
The pseudo-marginal approach for efficient Monte Carlo computations
Christophe Andrieu and G O Roberts · 2009
Cited alongside, same era.
Particle Markov Chain Monte Carlo
Roman Holenstein · 2009
Cited alongside, same era.
An overview of sequential Monte Carlo methods for parameter estimation in general state-space models
Nicholas Kantas, Arnaud Doucet, Sumeetpal Sindhu Singh, and Jan Marian Maciejowski · 2009
Cited alongside, same era.
Particle Markov chain Monte Carlo methods
Christophe Andrieu, Arnaud Doucet, and Roman Holenstein · 2010
Cited alongside, same era.
A tutorial on particle filtering and smoothing: fifteen years later
Arnaud Doucet and Adam M Johansen · 2010
Cited alongside, same era.
Uniform Ergodicity of the Iterated Conditional SMC and Geometric Ergodicity of Particle Gibbs samplers
Christophe Andrieu, Anthony Lee, and Matti Vihola · 2013
On the particle Gibbs sampler
Nicolas Chopin and Sumeetpal S Singh · 2013
Later among the works it cites.
Particle filters
Hans R Künsch · 2013
Later among the works it cites.
Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
Christophe Andrieu and Matti Vihola · 2014
Later among the works it cites.
Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
Anthony Lee and Krzysztof Latuszynski · 2014
Later among the works it cites.
Uniform ergodicity of the Particle Gibbs sampler
Fredrik Lindsten, Randal Douc, and E Moulines · 2014
Later among the works it cites.
On the role of interaction in sequential Monte Carlo algorithms
Nick Whiteley, Anthony Lee, and Kari Heine · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Closest in time.