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This is a video about Model Uncertainty and Uncertainty Quantification

Model Uncertainty and Uncertainty Quantification

August 20, 2018
Length: 48 minutes 9 seconds

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The Bayesian paradigm provides a coherent approach for quantifying uncertainty given available data and prior information. Aspects of uncertainty that arise in practice include uncertainty regarding parameters within a model, the choice of model, and propagation of uncertainty in parameters and models for predictions. In this talk I will present Bayesian approaches for addressing model uncertainty given a collection of competing models including model averaging and ensemble methods that potentially use all available models and will highlight computational challenges that arise in implementation of the paradigm.

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