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Activity Number: 419 - Bayesian Computation and Spatial Modeling
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329867 Presentation
Title: Bayesian Spatial Clustering with Particle Optimization
Author(s): Sameer Deshpande* and Cecilia Balocchi and Shane Jensen and Edward George
Companies: University of Pennsylvania, Wharton Statistics and University of Pennsylvania, Wharton Statistics and The Wharton School, University of Pennsylvania and Wharton, University of Pennsylvania
Keywords: Conditional autoregressive models; Bayesian shrinkage; Clustering; Spatial correlation

Bayesian hierarchical models of spatial data often use priors that smooth across geographically proximate areal units. In complex urban environments, there may be sharp boundaries intrinsic to the geography or population distribution that results in distinct clusters of areal units exhibiting markedly different trends. Typically, this partition is unknown a priori and the usual stochastic search techniques are computationally prohibitive since these searches must explore a vast discrete space of possible partitions.

In this work, rather than directly sampling from the posterior distribution of partitions, we introduce an ensemble optimization procedure targeting the partitions with largest posterior probability. We run several greedy searches over the posterior distribution of partitions that are made ``mutually aware'' through a penalty that repels search trajectories that are headed to the same point. We demonstrate our method with simulated data and a case study about the crime rate in the city of Philadelphia.

Authors who are presenting talks have a * after their name.

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