An Invitation to Sequential Monte Carlo Samplers

2022/06/08 by Chenguang Dai, Jeremy Heng, Pierre Jacob +1 · 1 voice · 4 citations
Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference #Gaussian Processes and Bayesian Inference

paper · doi:10.1080/01621459.2022.2087659

openalex publication_date 2022/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits. Supplementary materials for this article are available online.

Citations

• Annealed importance sampling
• Non-asymptotic Error Bounds for Sequential MCMC Methods in Multimodal Settings
• GPU acceleration of the particle filter: the Metropolis resampler
• Non-asymptotic Error Bounds for Sequential MCMC and Stability of Feynman-Kac Propagators
• Can local particle filters beat the curse of dimensionality?
• Nested Sequential Monte Carlo Methods
• Estimation and Inference in Two-Step Econometric Models
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Sequential Monte Carlo for Graphical Models
• Asynchronous Anytime Sequential Monte Carlo
• Obstacles to High-Dimensional Particle Filtering
• Gibbs flow for approximate transport with applications to Bayesian computation
• An MCMC-free approach to post-selective inference
• Unbiased Markov chain Monte Carlo with couplings
• Unbiased and Consistent Nested Sampling via Sequential Monte Carlo
• Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation
• Unbiased Smoothing using Particle Independent Metropolis-Hastings
• Stratification and Optimal Resampling for Sequential Monte Carlo
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
• Continual Repeated Annealed Flow Transport Monte Carlo

Cited by

• Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
• Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
• A Diffusion Monte Carlo algorithm employing depth first traversal and a stack instead of a swarm
• Particle Data Cloning for Complex Ordinary Differential Equations

Discussions

• A helpful introduction to "modern" SMC doi.org/10.1080/0162... [bsky, 0 points, 0 comments]

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