Complex models typically involve intractable likelihood functions which, from a Bayesian perspective, lead to intractable posterior distributions. In this context, Approximate Bayesian computation (ABC) methods can be used in order to obtain a valid posterior approximation. However, when simulation from the model is computationally demanding, then the ABC approach may be cumbersome. We discuss an alternative method, where the intractable likelihood is approximated by a quasi-likelihood calculated through an algorithm that is reminiscent of the ABC. The proposed approximation method requires less computational effort than ABC. An extension to multiparameter models is also considered and the method is illustrated by several examples.

A Quasi likelihood approximation of posterior distributions for likelihood-intractable complex models

CABRAS, STEFANO;Castellanos, ME;
2014

Abstract

Complex models typically involve intractable likelihood functions which, from a Bayesian perspective, lead to intractable posterior distributions. In this context, Approximate Bayesian computation (ABC) methods can be used in order to obtain a valid posterior approximation. However, when simulation from the model is computationally demanding, then the ABC approach may be cumbersome. We discuss an alternative method, where the intractable likelihood is approximated by a quasi-likelihood calculated through an algorithm that is reminiscent of the ABC. The proposed approximation method requires less computational effort than ABC. An extension to multiparameter models is also considered and the method is illustrated by several examples.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/110705
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