Petascale Hierarchical Modeling VIA Parallel Execution

Technical Report · Mon Apr 14 00:00:00 EDT 2014

DOI:https://doi.org/10.2172/1127434· OSTI ID:1127434

Gelman, Andrew ^[1]

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1. Principal Investigator

The research allows more effective model building. By allowing researchers to fit complex models to large datasets in a scalable manner, our algorithms and software enable more effective scientific research. In the new area of “big data,” it is often necessary to fit “big models” to adjust for systematic differences between sample and population. For this task, scalable and efficient model-fitting tools are needed, and these have been achieved with our new Hamiltonian Monte Carlo algorithm, the no-U-turn sampler, and our new C++ program, Stan. In layman’s terms, our research enables researchers to create improved mathematical modes for large and complex systems.

• Research Organization: Columbia Univ., New York, NY (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: SC0002099
• OSTI ID: 1127434
• Report Number(s): DOE-DE-SC0002099
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING