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Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)
Author(s) -
Beskos Alexandros,
Papaspiliopoulos Omiros,
Roberts Gareth O.,
Fearnhead Paul
Publication year - 2006
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2006.00552.x
Subject(s) - inference , computer science , maximum likelihood , monte carlo method , bayesian probability , diffusion , maximum likelihood sequence estimation , bayesian inference , markov chain monte carlo , likelihood function , estimation theory , algorithm , statistical physics , mathematics , artificial intelligence , statistics , physics , thermodynamics
Summary. The objective of the paper is to present a novel methodology for likelihood‐based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.