Theory of segmented particle filters

DOI10.1017/apr.2015.7zbMath1338.65023OpenAlexW2296463696MaRDI QIDQ2806346

Chiang-Wee Heng, Ajay Jasra, Hock Peng Chan

Publication date: 17 May 2016

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aap/1457466156

zbMATH Keywords

algorithmnumerical exampleparallel processingsequential Monte Carloparticle filterstandard error estimationestimation of hidden Markov models

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Parallel numerical computation (65Y05)

Related Items

Divide-and-conquer Bayesian inference in hidden Markov models ⋮ Biased online parameter inference for state-space models ⋮ Stochastic Gradient MCMC for State Space Models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Likelihood computation for hidden Markov models via generalized two-filter smoothing
• Limit theorems for weighted samples with applications to sequential 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
• Inference in hidden Markov models.
• A general theory of particle filters in hidden Markov models and some applications
• Twisted particle filters
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• A sequential smoothing algorithm with linear computational cost
• On parallel implementation of sequential Monte Carlo methods: the island particle model