Premium
Approximate Bayesian computation using indirect inference
Author(s) -
Drovandi Christopher C.,
Pettitt Anthony N.,
Faddy Malcolm J.
Publication year - 2011
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2010.00747.x
Subject(s) - approximate bayesian computation , inference , computer science , likelihood function , bayesian inference , statistical inference , indirect inference , bayesian probability , computation , particle filter , monte carlo method , algorithm , mathematics , statistics , artificial intelligence , estimation theory , estimator , kalman filter
Summary. We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.