Automatically adapting the number of state particles in SMC$^2$

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Automatically adapting the number of state particles in SMC $^2$

27 January 2022

Christopher C. Drovandi

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Papers citing "Automatically adapting the number of state particles in SMC$^2$"

9 / 9 papers shown

Title
Accelerating sequential Monte Carlo with surrogate likelihoods Joshua J. Bon Anthony Lee Christopher C. Drovandi 66 19 0 08 Sep 2020
Robustly estimating the marginal likelihood for cognitive models via importance sampling Minh-Ngoc Tran Marcel Scharth David Gunawan Robert Kohn Scott D. Brown Guy E. Hawkins 32 15 0 14 Jun 2019
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter Christopher C. Drovandi R. Everitt Andrew Golightly D. Prangle 81 14 0 05 Jun 2019
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo R. Salomone Leah F. South A. M. Johansen Christopher C. Drovandi Dirk P. Kroese 70 34 0 10 May 2018
Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment Victor Elvira Joaquín Míguez Petar M. Djurić 63 71 0 16 Sep 2015
On the efficiency of pseudo-marginal random walk Metropolis algorithms Chris Sherlock Alexandre Hoang Thiery Gareth O. Roberts Jeffrey S. Rosenthal 123 191 0 27 Sep 2013
Nested particle filters for online parameter estimation in discrete-time state-space Markov models Dan Crisan Joaquín Míguez 105 95 0 08 Aug 2013
SMC^2: an efficient algorithm for sequential analysis of state-space models Nicolas Chopin Pierre E. Jacob O. Papaspiliopoulos 103 357 0 07 Jan 2011
The pseudo-marginal approach for efficient Monte Carlo computations Christophe Andrieu Gareth O. Roberts 194 895 0 31 Mar 2009