Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering

DOI10.1007/s00180-012-0352-yzbMath1305.65062OpenAlexW2060182299MaRDI QIDQ2255925

Heikki Haario, Simo Särkkä, Isambi Sailon Mbalawata

Publication date: 18 February 2015

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-012-0352-y

zbMATH Keywords

stochastic differential equationparameter estimationMarkov chain Monte CarloKalman filterHamiltonian Monte Carlomatrix fraction decomposition

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (10)

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Uses Software

• BayesDA

Cites Work

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• Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms
• Bayesian system identification via Markov chain Monte Carlo techniques
• Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
• Bayesian inference for nonlinear multivariate diffusion models observed with error
• Statistical decision theory and Bayesian analysis. 2nd ed
• Parameter estimation for continuous-time models - a survey
• Estimating the parameters of stochastic differential equations
• Derivatives of the matrix exponential and their computation
• Inference in hidden Markov models.
• Closed-form likelihood expansions for multivariate diffusions
• Optimal tuning of the hybrid Monte Carlo algorithm
• Probability density estimation in stochastic environmental models using reverse representa\-tions
• Stochastic processes and filtering theory
• Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach
• Practical Markov Chain Monte Carlo
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Advanced Markov Chain Monte Carlo Methods
• Estimation for nonlinear stochastic differential equations by a local linearization method¹
• On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations*
• Likelihood Inference for Discretely Observed Nonlinear Diffusions
• Particle Markov Chain Monte Carlo Methods
• On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
• Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach
• Explicit variable step-size and time-reversible integration
• Stochastic differential equations. An introduction with applications.

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