Bayesian inference for nonlinear multivariate diffusion models observed with error

Diffusion processes governed by stochastic differential equations (SDEs) are a well-established tool for modelling continuous time data from a wide range of areas. Consequently, techniques have been developed to estimate diffusion parameters from partial and discrete observations. Likelihood-based i...

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Veröffentlicht in:	Computational statistics & data analysis Jg. 52; H. 3; S. 1674 - 1693
Hauptverfasser:	Golightly, A., Wilkinson, D.J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 2008 Elsevier Science Elsevier
Schriftenreihe:	Computational Statistics & Data Analysis
Schlagworte:	Bayesian inference Exact sciences and technology General topics Innovation scheme Mathematics MCMC Multivariate analysis Nonlinear stochastic differential equation Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Parametric inference Particle filter Probability and statistics Reparameterisation Sciences and techniques of general use Statistics MCMC Nonlinear stochastic differential equation Innovation scheme Bayesian inference Reparameterisation Particle filter Density estimation Parameter estimation Mixing Statistical distribution Error estimation Differential equation Continuous time Multivariate analysis Stochastic method Stochastic process Markov chain Distribution function Diffusion process Integral equation Approximation theory Latent value Measurement error Posterior distribution Stochastic equation Bayes estimation Mixed distribution Monte Carlo method Data analysis Approximation Discrete data Statistical estimation Numerical analysis Statistical computation Non linear model
ISSN:	0167-9473, 1872-7352
Online-Zugang:	Volltext
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