Inference in high-dimensional parameter space.

Abstract

Model parameter inference has become increasingly popular in recent years in the field of computational epidemiology, especially for models with a large number of parameters. Techniques such as Approximate Bayesian Computation (ABC) or maximum/partial likelihoods are commonly used to infer parameters in phenomenological models that best describe some set of data. These techniques rely on efficient exploration of the underlying parameter space, which is difficult in high dimensions, especially if there are correlations between the parameters in the model that may not be known a priori. The aim of this article is to demonstrate the use of the recently invented Adaptive Metropolis algorithm for exploring parameter space in a practical way through the use of a simple epidemiological model.