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.

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

References in corpus (17)

• The pseudo-marginal approach for efficient Monte Carlo computations
• Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
• Can local particle filters beat the curse of dimensionality?
• Recursive Monte Carlo filters: Algorithms and theoretical analysis
• On the efficiency of pseudo-marginal random walk Metropolis algorithms
• Sequential Monte Carlo smoothing for general state space hidden Markov models
• A random map implementation of implicit filters
• Approximate Methods for State-Space Models
• Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
• Local antithetic sampling with scrambled nets
• On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler
• GPU acceleration of the particle filter: the Metropolis resampler
• The limiting process of $N$-particle branching random walk with polynomial tails
• A Stable Particle Filter in High-Dimensions
• Lattice Particle Filters
• Fully Parallel Particle Learning for GPGPUs and Other Parallel Devices
• vSMC: Parallel Sequential Monte Carlo in C++

Cited by in corpus (26)

• The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting
• Approximate Bayesian computation with the Wasserstein distance
• Particle Filters and Data Assimilation
• Particle Filtering with Invertible Particle Flow
• Compressed Monte Carlo with application in particle filtering
• Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
• Importance sampling type estimators based on approximate marginal MCMC
• Convergence guarantees for kernel-based quadrature rules in misspecified settings
• Spatial field reconstruction with INLA: Application to IFU galaxy data
• A probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems
• Approximating Bayes in the 21st Century
• Bayesian Inference for State Space Models using Block and Correlated Pseudo Marginal Methods
• The Block Pseudo-Marginal Sampler
• Quasi-Monte Carlo methods applied to tau-leaping in stochastic biological systems
• Computing Bayes: From Then 'Til Now'
• On embedded hidden Markov models and particle Markov chain Monte Carlo methods
• Asymptotic Normality of Extensible Grid Sampling
• Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
• Extensible grids: uniform sampling on a space-filling curve
• Stratification and Optimal Resampling for Sequential Monte Carlo
• Tree-based Particle Smoothing Algorithms in a Hidden Markov Model
• A table of short-period Tausworthe generators for Markov chain quasi-Monte Carlo
• Population Quasi-Monte Carlo
• A search for short-period Tausworthe generators over ${\mathbb{F}}_b$ with application to Markov chain quasi-Monte Carlo
• A Scrambled Method of Moments
• A weighted Discrepancy Bound of quasi-Monte Carlo Importance Sampling