Recent Developments and Future Directions in Bayesian Model Averaging

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2020-02-07 (Creation date: 2020-02-07)
Main contributor
David Kaplan
From a Bayesian point of view, the selection of a particular model from a universe of possible models can be characterized as a problem of uncertainty. The method of Bayesian model averaging quantifies model uncertainty by recognizing that not all models are equally good from a predictive point of view. Rather than choosing one model and assuming that the chosen model is the one that generated the data Bayesian model averaging obtains a weighted combination of the parameters of a subset of possible models, weighted by each models’ posterior model probability. This workshop provides an overview of Bayesian model averaging with a focus on recent developments and applications to propensity score analysis, missing data, and probabilistic forecasting of relevance to social science research.
IU Workshop in Methods
Workshop in Methods
Social Science Research Commons
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David Kaplan is the Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin – Madison. His research focuses on the development of Bayesian statistical methods for education research. His work on these topics is directed toward applications to large-scale cross-sectional and longitudinal survey designs.

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