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 inference can be problematic as closed form transition densities are rarely available. One widely used solution

• Type: Journal
• Year: 2008
• Where: CSDA
• Authors: Andrew Golightly, Darren J. Wilkinson
• Keywords: Continuous Time Data, CSDA 2008, Diffusion, Form Transition Densities