Finite-sample complexity of sequential Monte Carlo estimators

DOI10.1214/23-aos2295arXiv1803.09365OpenAlexW2795211099MaRDI QIDQ6177328

Joseph Mathews, Scott C. Schmidler, Joe Marion

Publication date: 31 August 2023

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.09365

zbMATH Keywords

computational complexitysequential Monte CarloBayesian computation

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15)

Related Items (1)

Finite sample complexity of sequential Monte Carlo estimators on multimodal target distributions

Cites Work

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• On the convergence of adaptive sequential Monte Carlo methods
• Sequential Monte Carlo for rare event estimation
• A nonasymptotic theorem for unnormalized Feynman-Kac particle models
• Limit theorems for weighted samples with applications to sequential Monte Carlo methods
• General state space Markov chains and MCMC algorithms
• Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
• Sufficient conditions for torpid mixing of parallel and simulated tempering
• Random generation of combinatorial structures from a uniform distribution
• Central limit theorem for nonlinear filtering and interacting particle systems
• Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters
• Error bounds for sequential Monte Carlo samplers for multimodal distributions
• Quantitative approximations of evolving probability measures and sequential Markov chain Monte Carlo methods
• Recursive Monte Carlo filters: algorithms and theoretical analysis
• Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
• On the stability of sequential Monte Carlo methods in high dimensions
• Sequential Monte Carlo Samplers
• The geometry of logconcave functions and sampling algorithms
• Hit-and-run from a corner
• Random walks and anO*(n5) volume algorithm for convex bodies
• A sequential particle filter method for static models
• Sequential Monte Carlo Samplers: Error Bounds and Insensitivity to Initial Conditions
• Forest resampling for distributed sequential Monte Carlo
• Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments
• Stability of sequential Markov Chain Monte Carlo methods
• Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities
• On parallel implementation of sequential Monte Carlo methods: the island particle model

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