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Activity Number:
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229
- Geostatistical Computing on Modern Parallel Architectures
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract #323343
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Title:
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Distributed Inference for a Spatial Bayesian Network with Application to Natural Hazard Risk Assessment
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Author(s):
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Christopher Krapu* and Nolan Hayes and Robert Stewart and Amy Rose and Alexandre Sorokine and Kuldeep Kurte
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Companies:
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Oak Ridge National Laboratory and Oak Ridge National Laboratory and Oak Ridge National Laboratory and Oak Ridge National Laboratory and Oak Ridge National Laboratory and Oak Ridge National Laboratory
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Keywords:
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MCMC;
Distributed inference;
Spatial statistics;
Bayesian networks
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Abstract:
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Major challenges for modeling opportunistically sampled real-world data are a high degree of missingness, strong sampling bias, as well as inherent spatial autocorrelation. To address these issues, we propose a novel graphical model for a spatial Bayesian network which combines a dimension-reduced latent Gaussian spatial field with parameters enforcing a DAG-derived cross-variable covariance structure which is amenable to usage of prior information derived from expert elicitation. To perform inference using large datasets with frequent missing data, we implement a distributed Gibbs sampling scheme composed of alternating steps of data augmentation and Hamiltonian Monte Carlo in PyMC3. We present a case study on modeling the properties of buildings for natural hazard risk assessment in Washington, D.C.
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Authors who are presenting talks have a * after their name.
