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Activity Number: 8 - Computational Methods and Bayesian Inference for Networks
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Council of Chapters
Abstract #326938 Presentation
Title: High-Dimensional MCMC Diagnostics with Application to Spatial Text Clustering of Beer Flavours
Author(s): David Alexander Campbell* and Subhash Lele and Peter Solymos
Companies: Simon Fraser University and University of Alberta and Alberta Biodiversity Monitoring
Keywords: MCMC Diagnostics; High Dimensional Data; General Additive Models
Abstract:

With hundreds of beer styles and thousands of breweries in hundreds of countries, a model that describes the flavour preferences for beer inherently has a large number of parameters. Using a hierarchical Bayesian model for spatial-flavour preference clusters based on text beer reviews as a high dimensional application, we present a technique for high dimensional MCMC diagnostics. Standard diagnostic tools such as Gelman-Rubin, Geweke, and other MCMC Diagnostics were designed for assessing convergence one parameter at a time. The proposed approach uses multiple MCMC chains and provides a test for convergence by using a classifier based on General Additive Models.


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