= Andrew Gelman
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<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 analysis>[regression], computation and statistical graphics. His Columbia research page describes applications ranging from survey inference and elections to environmental and medical measurements.https://sites.stat.columbia.edu/gelman/[Author homepage, research areas and books]{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 <statistical parameter>[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.
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{title=Portrait of <Andrew Gelman>}
{description=Andrewgelman, 13 May 2026. https://creativecommons.org/licenses/by/4.0/[CC BY 4.0], via Wikimedia Commons.}
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