Statistical Inference for Implicit Models using Bayesian Synthetic Likelihood
Date: 9 September 2021, Thursday
Time: 12pm AEST
Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au
Speaker: Prof Christopher Drovandi (QUT)
Abstract:
Implicit models are defined as those that can be simulated but the associated likelihood function is intractable. Such models are prevalent in many fields such as biology, ecology, cosmology and epidemiology. Given the unavailability of the likelihood function, statistical inference for implicit models is challenging as we must rely only on the ability to generate mock datasets from the model of interest, and compare it with the observed data in some way. This talk will explain a useful method called Bayesian synthetic likelihood for conducting such statistical inference. I will discuss how BSL can be extended to reduce the number of model simulations required and to make it more robust to model misspecification. I will also describe some theoretical properties of the method.
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