The National Science Foundation awarded a 3-year, $300,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Regents of the University of California, Berkeley. The grant funds the development of a software infrastructure to facilitate the use of two cutting-edge Bayesian sampling methods - Preconditioned Optimized Convex Monte Carlo (POCOMC) and Microcanonical Hamiltonian Monte Carlo - by a range of scientists across disciplines such as astronomy, physics, data science, and statistics. These new sampling techniques are anticipated to provide significant computational cost reductions compared to existing alternatives, enabling faster and more accurate Bayesian uncertainty quantification. The project aims to deploy the samplers as standalone packages and integrate them into widely-used probabilistic programming languages, with the goal of revolutionizing statistical inference methods used in science and engineering.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $300.0k | 7/21/23 |