Gaussian Processes for Inference with Implicit Likelihoods

Опубликовано 27 июля 2016, 23:50

Complex deterministic and stochastic models are often used to describe dynamic systems in climate science, ecology and biology. 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: likelihood functions that are intractable or only implicitly specified by a computer model; computationally expensive model simulations; and high- dimensional, multivariate observations and model output. I will outline computationally expedient Gaussian process-based inferential approaches in the context of two very different models, a deterministic Earth- system model used in climate science, and a stochastic spatial model for infectious diseases. I will point out some of the common features between the two, but also highlight significant differences in the modeling frameworks and inferential goals. This talk is based on joint work with K. Sham Bhat (Los Alamos National Labs), Roman Jandarov (Dept. of Statistics, Penn State University [PSU]), Roman Tonkonojenkov (Dept. of Geosciences, PSU), Klaus Keller (Dept. of Geosciences, PSU), Ottar Bjornstad (Center for Infectious Disease Dynamics, PSU), and Bryan Grenfell (Ecology and Evolutionary Biology, Princeton University)