 Approximation of the posterior density for diffusion processesJ.A. Cano, M. Kessler and D. Salmerón Statistics & Probability Letters, 2006, vol. 76, issue 1, 39-44 Abstract: Bayesian inference for diffusion processes faces the same problem as likelihood-based inference: the transition density is not tractable, and as a consequence neither the likelihood nor the posterior density are computable. A natural solution adopted by several authors consists in considering the approximate posterior based on an Euler scheme approximation of the transition density. In this paper, we address the quality of the resulting approximation to the exact but intractable posterior. On one hand, we prove under global assumptions the weak convergence of the approximate posterior to the true posterior as the number of intermediate points used in the Euler scheme grows to infinity. On the other hand, we study in detail the Ornstein-Uhlenbeck process where some surprising results are obtained when a non-informative prior is used. Keywords: Diffusion; process; Bayesian; inference; Euler; scheme (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:76:y:2006:i:1:p:39-44 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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