Particle-kernel estimation of the filter density in state-space models

DOI10.3150/13-BEJ545zbMath1346.60112arXiv1111.5866OpenAlexW3122092251MaRDI QIDQ470055

Joaquín Míguez, Dan Crisan

Publication date: 11 November 2014

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.5866

zbMATH Keywords

Markov systemsalmost sure convergencedensity estimationstate-space modelsparticle filteringstochastic filteringsequential Monte Carlo method

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Density estimation (62G07) Filtering in stochastic control theory (93E11) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Strong limit theorems (60F15) Signal detection and filtering (aspects of stochastic processes) (60G35) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Numerical analysis or methods applied to Markov chains (65C40) Stochastic particle methods (65C35)

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Uses Software

• KernSmooth
• simannf90
• pyuvdata

Cites Work

• On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization
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• On the Estimation of Differential Entropy From Data Located on Embedded Manifolds
• Uniform approximations of discrete-time filters
• Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
• On the asymptotic properties of a simple estimate of the Mode
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• A Basic Convergence Result for Particle Filtering
• On Accelerated Random Search
• A survey of convergence results on particle filtering methods for practitioners
• Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
• Edgeworth Approximation of Multivariate Differential Entropy
• Maximum a posteriori sequence estimation using Monte Carlo particle filters
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