Master Class: Parameterization and Bayesian modeling

SpeakerAndrew Gelman
AffiliationColumbia University
DateTuesday, 15 Apr 2014
Time12:00 - 13:00
LocationGalton LT (Room 115), 1-19 Torrington Place
Event seriesMaster Class: Andrew Gelman (14 - 16 April 2014)

Progress in statistical computation often leads to advances in statistical modeling. For example, it is surprisingly common that an existing model is reparameterized, solely for computational purposes, but then this new configuration motivates a new family of models that is useful in applied statistics. One reason why this phenomenon may not have been noticed in statistics is that reparameterizations do not change the likelihood. In a Bayesian framework, however, a transformation of parameters typically suggests a new family of prior distributions. We discuss examples in censored and truncated data, mixture modeling, multivariate imputation, stochastic processes, and multilevel models.

All welcome, no registration required to attend this talk.

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