Sampling-Assisted Inference of Intractable Models

Sampling-Assisted Inference of Intractable Models

Likelihood function plays the fundamental role in most statistical analysis. Nevertheless, when the likelihood is too complex to compute in traditional ways, indirect inference or Approximate Bayesian Computation (ABC) is often adopted.

This paper proposes a new approach, based on the Hermite polynomial expansion, to estimate the intractable likelihood function. Based on the estimated likelihood function, a maximum likelihood estimate and its large sample properties are achieved. Comparing to other existing methods, the new method provides more accurate estimation and are justified by asymptotic theories. It does not require extra auxiliary models which are necessary in indirect methods and are often not easy to find.

The new method also has a very close link to ABC in the sense that it also simulates many pseudo data sets, however it does not need to compare the pseudo data with the raw data as what ABC does. Therefore, the new method does not suffer from the drawbacks of ABC, for example there are no hard rules in ABC about how to choose the distance function, data summary statistics and threshold for acceptance.

Speaker

 Bo Zhang, PhD Student, University of Essex

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk).