NDLI: Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC

Content Provider	Springer Nature Link
Author	Särkkä, Simo Hartikainen, Jouni Mbalawata, Isambi Sailon Haario, Heikki
Copyright Year	2013
Abstract	This article is concerned with Bayesian estimation of parameters in non-linear multivariate stochastic differential equation (SDE) models occurring, for example, in physics, engineering, and financial applications. In particular, we study the use of adaptive Markov chain Monte Carlo (AMCMC) based numerical integration methods with non-linear Kalman-type approximate Gaussian filters for parameter estimation in non-linear SDEs. We study the accuracy and computational efficiency of gradient-free sigma-point approximations (Gaussian quadratures) in the context of parameter estimation, and compare them with Taylor series and particle MCMC approximations. The results indicate that the sigma-point based Gaussian approximations lead to better approximations of the parameter posterior distribution than the Taylor series, and the accuracy of the approximations is comparable to that of the computationally significantly heavier particle MCMC approximations.
Starting Page	427
Ending Page	437
Page Count	11
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	25
Issue Number	2
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2013-12-13
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stochastic differential equation Parameter estimation Gaussian approximation Non-linear Kalman filter Adaptive Markov chain Monte Carlo Statistics and Computing/Statistics Programs Artificial Intelligence (incl. Robotics) Statistical Theory and Methods Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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