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Published on: June 3, 2009
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data
Andrew J Holbrook1, Charles E Loeffler2, Seth R Flaxman3
1Department of Biostatistics, University of California, Los Angeles, Los Angeles, USA.
Statistics and Computing
|August 6, 2021
Summary
This study introduces a parallelized computational framework for Hawkes process models, significantly speeding up analysis of self-exciting phenomena. The new approach enables large-scale Bayesian analysis of complex datasets, such as crime patterns.
Area of Science:
- Computational Statistics
- Data Science
- Epidemiology
Background:
- Hawkes processes model self-exciting events like pandemics and earthquakes.
- Their computational complexity hinders analysis of large datasets.
Purpose of the Study:
- To develop a high-performance computing framework for accelerating Hawkes process calculations.
- To enable Bayesian analysis of large-scale, self-exciting event data.
Main Methods:
- Parallelization of likelihood evaluations using central and graphics processing units.
- Implementation of an adaptive Metropolis-Hastings scheme for Bayesian analysis.
- Development of the open-source R package 'hpHawkes'.
Main Results:
- Achieved over 100-fold speedups in computation compared to single-core processing.
- Successfully applied the framework to a large dataset of gunshot incidents (over 85,000 observations).
- Extended previous analyses of the same data by an order of magnitude.
Conclusions:
- The developed framework significantly reduces computational burden for Hawkes processes.
- High-performance computing enables large-scale Bayesian inference in self-exciting phenomena.
- The 'hpHawkes' package facilitates broader application of these advanced statistical models.
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