New Methods for Scalable and Robust Simulation-Based Inference — NSF Award to University of Illinois at Urbana-Champaign (IL, $155
In fields like genetics, ecology, biology, economics, and psychology, scientists use complex structural models to better understand how the world works. These models aim to mimic real systems, such as how species interact, how crops grow, or how diseases spread, and often rely on key input values, or parameters, that n
| Award title | New Methods for Scalable and Robust Simulation-Based Inference |
|---|---|
| Award ID | 2515542 |
| Awardee | University of Illinois at Urbana-Champaign |
| City | URBANA |
| State | IL |
| Amount obligated | $155,000 |
| Principal investigator | Yuexi Wang |
| Program | STATISTICS |
| Start date | 09/01/2025 |
| Abstract | In fields like genetics, ecology, biology, economics, and psychology, scientists use complex structural models to better understand how the world works. These models aim to mimic real systems, such as how species interact, how crops grow, or how diseases spread, and often rely on key input values, or parameters, that need to be estimated from data. However, many of these models involve high-dimensional, richly structured parameter spaces, making traditional likelihood-based inference methods inf |
| Source | NSF Awards |
$799/mo
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