Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method

DOI10.1016/j.csda.2010.04.018zbMath1247.62212OpenAlexW1973431172WikidataQ60398370 ScholiaQ60398370MaRDI QIDQ452571

J. Herrera, D. Rodríguez-Gómez

Publication date: 15 September 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2010.04.018

zbMATH Keywords

stochastic differential equationfinite element methodhidden Markov modelsequential Monte Carlononlinear state estimationpoint-mass filter

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Diffusion processes (60J60)

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Uses Software

• Triangle

Cites Work

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• Digital synthesis of non-linear filters
• Advanced point-mass method for nonlinear state estimation
• Bayesian inference for nonlinear multivariate diffusion models observed with error
• Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models
• Parameter estimation in general state-space models using particle methods
• Inference in hidden Markov models.
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• The finite element method after twenty-five years: A personal view

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