Developing Conjugate Models for Exact MCMC free Bayesian Inference with Application to Hig — NSF Award to University of Missouri-C
The massive expansion in the production of data has led to natural computational challenges in uncertainty quantification. In particular, Bayesian methodology can account for sources of uncertainty, but requires techniques known to be computationally demanding. These difficulties are exacerbated when data are spatially
| Award title | Developing Conjugate Models for Exact MCMC free Bayesian Inference with Application to Hig |
|---|---|
| Award ID | 2547531 |
| Awardee | University of Missouri-Columbia |
| City | COLUMBIA |
| State | MO |
| Amount obligated | $38,121 |
| Principal investigator | Jonathan Bradley |
| Program | STATISTICS |
| Start date | 08/01/2025 |
| Abstract | The massive expansion in the production of data has led to natural computational challenges in uncertainty quantification. In particular, Bayesian methodology can account for sources of uncertainty, but requires techniques known to be computationally demanding. These difficulties are exacerbated when data are spatially and/or temporally correlated. The current solutions predominantly use either approximations or inefficient iterative methods such as Markov chain Monte Carlo (MCMC). This project |
| Source | NSF Awards |
$799/mo
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