Bayesian Learning for Spatial Point Processes: Theory, Methods, Computation, and Applicati — NSF Award to The University of Texas
Scientists, engineers, economists, and sports practitioners are increasingly aware of the importance of accurately understanding underlying clusters when trying to recover complex patterns that vary across time and space. Examples of such patterns include earthquake occurrences over North America, tree locations in Bar
| Award title | Bayesian Learning for Spatial Point Processes: Theory, Methods, Computation, and Applicati |
|---|---|
| Award ID | 2412923 |
| Awardee | The University of Texas Health Science Center at Houston |
| City | HOUSTON |
| State | TX |
| Amount obligated | $121,153 |
| Principal investigator | Guanyu Hu |
| Program | STATISTICS |
| Start date | 12/15/2023 |
| Abstract | Scientists, engineers, economists, and sports practitioners are increasingly aware of the importance of accurately understanding underlying clusters when trying to recover complex patterns that vary across time and space. Examples of such patterns include earthquake occurrences over North America, tree locations in Barro Colorado Island, field goal attempts of professional players over basketball courts, and bullet-screen comments from live streams. When performing statistical analysis on such c |
| Source | NSF Awards |
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