Yale University
Department of Statistics and Biostatistics
Bayesian model-building by pure thought: some principles and examples
Monday, November 4, 1996
Andrew Gelman
Department of Statistics
Columbia University
Seminar to be held in Room 309, LEPH, 60 College
In applications, statistical models are often restricted to what produces
reasonable estimates based on the data at hand. In many cases, however,
models can be restricted based on theoretical arguments alone, in the
absence of any data and with minimal applied context. We illustrate with
theoretical examples from spatial statistics and time series.
These theoretical arguments may have important practical consequences when
working with models of open-ended complexity. We discuss briefly the
implications in applied contexts including toxicokinetics, election
forecasting, and opinion polling.