Murali Haran
Penn State University

Gaussian processes for inference with implicit likelihoods

Complex deterministic and stochastic models are often used to describe dynamic systems in disciplines like climate science and infectious disease modeling. Inferring unknown parameters of these models is of interest, both for understanding the underlying scientific processes as well as for making valid predictions. Some of the challenges typically involved in inference for these models are: (i) intractable or implicit likelihood functions; (ii) computationally expensive model simulations; (iii) high-dimensional, multivariate model output and observations. I will describe an approach that uses a Gaussian process to fit simulations of the complex model at several different parameter settings, while also accounting for measurement error and a data-model discrepancy term. This method provides a computationally tractable approach for performing Bayesian inference for the parameters of interest. I will outline the application of this approach to climate models; this work is useful in making projections of the North Atlantic Meridional Overturning Circulation (AMOC). AMOC projections are of considerable interest since changes in the AMOC may result in major impacts on global climate. Time permitting, I will also briefly discuss a different Gaussian process-based approach used in the context of infectious disease modeling.