Sequential Monte Carlo sampling in hidden Markov models of nonlinear dynamical systems

DOI10.1016/j.amc.2014.02.012zbMath1334.65011OpenAlexW2031053958MaRDI QIDQ272665

Mihai Anitescu, Xiaoyan Zeng

Publication date: 20 April 2016

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2014.02.012

zbMATH Keywords

hidden Markov modelordinary differential equationsequential Monte Carlo methodschemical process

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Uses Software

• ADOL-C
• ADIC
• PETSc
• CVODES
• TAO
• SISTOS

Cites Work

• Hidden Markov models in finance
• Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems
• Recursive Monte Carlo filters: algorithms and theoretical analysis
• Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
• Monte Carlo strategies in scientific computing.
• Algorithm 755: ADOL-C
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