Gaussian Processes for Inference with Implicit Likelihoods


January 31, 2012


Murali Haran


Pennsylvania State University


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)


Murali Haran

I am an associate professor of statistics at Pennsylvania State University. I received my Ph.D. from the School of Statistics at the University of Minnesota and a B.S. in Computer Science from Carnegie Mellon University. I was a postdoctoral fellow at the National Institute of Statistical Sciences (courtesy appointment at Duke University) (2003–2004) and a New Research Fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI) program (2009–2010) on space-time analysis.

I am on sabbatical at the Department of Statistics, University of Washington, Seattle for the academic year 2011–2012. You can still reach me via my Penn State email address while I am away.

My research interests include statistical computing (Markov chain Monte Carlo algorithms) and models for spatial data and computer experiments (primarily involving Gaussian random fields and Bayesian hierarchical models), with applications to problems in climate science, disease modeling, ecology and epidemiology. I have also worked on statistical techniques called random forests for applications in software engineering research. For more, please see: background and more on my research.