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Activity Number:
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458
- Bayesian Methods in Spatial Statistics
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Type:
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Invited
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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WNAR
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Abstract #320383
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Title:
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Bayesian Nonparametric Learning for Spatial Point Process
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Author(s):
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Guanyu Hu*
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Companies:
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University of Missouri
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Keywords:
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Intensity Estimation;
Point Pattern;
Spatial Heterogeneity
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Abstract:
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Spatial point pattern data are routinely encountered. A flexible regression model for the underlying intensity is essential to characterizing the spatial point pattern. We present novel nonparametric Bayesian methods for learning the underlying intensity surface built upon nonparametric Bayesian methods. Our method has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate and the method is superior compared to a wide range of competing methods. Several applications such as earquake occurences and basketball shot charts are presented as illustrations of our proposed methods.
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Authors who are presenting talks have a * after their name.
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