An algorithm for approximating the second moment of the normalizing constant estimate from a particle filter

DOI10.1007/s11009-016-9513-8zbMath1382.93033arXiv1602.02279OpenAlexW2290837485WikidataQ59613496 ScholiaQ59613496MaRDI QIDQ1707044

Nick Whiteley, Svetoslav Kostov

Publication date: 28 March 2018

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.02279

zbMATH Keywords

hidden Markov modelmarginal likelihoodparticle filternormalizing constant

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Monte Carlo methods (65C05) Estimation and detection in stochastic control theory (93E10)

Related Items (1)

Multilevel estimation of normalization constants using ensemble Kalman-Bucy filters

Cites Work

• Unnamed Item
• Delayed acceptance particle MCMC for exact inference in stochastic kinetic models
• Stability properties of some particle filters
• A nonasymptotic theorem for unnormalized Feynman-Kac particle models
• A general theory of particle filters in hidden Markov models and some applications
• Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models
• Sequential Monte Carlo Methods in Practice
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation

This page was built for publication: An algorithm for approximating the second moment of the normalizing constant estimate from a particle filter