Machine Learning Coffee Seminar: "Likelihood-free inference and predictions for computational epidemiology"

Likelihood-free inference and predictions for computational epidemiology

 

Jukka Corander

Professor, University of Helsinki and University of Oslo

 

Abstract:

Simulator-based models often allow inference and predictions under more realistic assumptions than those employed in standard statistical models. For example, the observation model for an underlying stochastic process can be more freely chosen to reflect the characteristics of the data gathering procedure. A major obstacle for such models is the intractability of the likelihood, which has to a large extent hampered their practical applicability. I will discuss recent advances in likelihood-free inference that greatly accelerate the model fitting process by exploiting a combination of machine learning techniques. Applications to several novel models in infectious disease epidemiology are used to illustrate the potential offered by this approach.