Title
Functional ANOVA Models for Comparing Sources of Variability in Climate Model Output
Speaker
Cari Kaufman, University of California, Berkeley
Abstract
Functional analysis of variance (ANOVA) models partition a functional response according to the main effects and interactions of various factors. Motivated by the question of how to compare the sources of variability in climate models run under various conditions, we develop a general framework for functional ANOVA modeling from a Bayesian viewpoint, assigning Gaussian process prior distributions to each batch of functional effects. We discuss computationally efficient strategies for posterior sampling using Markov Chain Monte Carlo algorithms, and we emphasize useful graphical summaries based on the posterior distribution of model-based analogues of the traditional ANOVA decompositions of variance. We present a case study using these methods to analyze data from the Prudence Project, a climate model inter-comparison study providing ensembles of climate projections over Europe.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
