Three discussions of the paper "sequential quasi-Monte Carlo sampling", by M. Gerber and N. Chopin

Abstract

This is a collection of three written discussions of the paper "sequential quasi-Monte Carlo sampling" by M. Gerber and N. Chopin, following the presentation given before the Royal Statistical Society in London on December 10th, 2014.

Published in the Journal of the Royal Statistical Society, volume 77(3), pages 559, 569 and 570

References in corpus (8)

• The pseudo-marginal approach for efficient Monte Carlo computations
• Recursive Monte Carlo filters: Algorithms and theoretical analysis
• Sequential Monte Carlo smoothing for general state space hidden Markov models
• Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
• Local antithetic sampling with scrambled nets
• GPU acceleration of the particle filter: the Metropolis resampler
• A Stable Particle Filter in High-Dimensions
• Lattice Particle Filters

Cited by in corpus (10)

• Approximate Bayesian computation with the Wasserstein distance
• Particle Filters and Data Assimilation
• Compressed Monte Carlo with application in particle filtering
• Approximating Bayes in the 21st Century
• Bayesian Inference for State Space Models using Block and Correlated Pseudo Marginal Methods
• Computing Bayes: From Then 'Til Now'
• On embedded hidden Markov models and particle Markov chain Monte Carlo methods
• Population Quasi-Monte Carlo
• A Scrambled Method of Moments
• A search for short-period Tausworthe generators over ${\mathbb{F}}_b$ with application to Markov chain quasi-Monte Carlo