 Andrew Gelman

Andrew Gelman works on Bayesian statistics, statistical modelling and the interpretation of data in scientific and social questions. His research and teaching connect hierarchical Bayesian models, regression, computation and statistical graphics. His Columbia research page describes applications ranging from survey inference and elections to environmental and medical measurements.^[ref]

A characteristic modelling problem is learning about many related groups: completely separate estimates can be noisy, while a single common estimate can hide differences. A hierarchical Bayesian model estimates group parameters together and lets the data influence their degree of pooling. Model checking, including posterior predictive checks, then asks whether simulated observations reproduce scientifically relevant features of the actual data. This connects fitting a model with examining the adequacy of its assumptions.

Andrew Gelman is an American statistician and professor known for his work in statistical methodology, particularly in the fields of Bayesian statistics, multilevel modeling, and political science. He is a professor at Columbia University, where he is affiliated with both the Department of Statistics and the Department of Political Science. Gelman has authored several influential books and numerous research papers and is recognized for his efforts to improve statistical practices and communicate complex statistical ideas clearly.