The National Science Foundation (NSF), through its Division of Mathematical Sciences, awarded a $225,000 Project Grant to The Leland Stanford Junior University (Stanford University) to develop new algorithms for Bayesian computation. The grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), aims to address challenges in Bayesian inference for complex statistical models, such as hidden Markov models with continuous variables and models with intractable likelihood...
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,...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to Carnegie Mellon University to develop a methodology for simulation-based inference that uses random features rather than carefully designed summary statistics. The 3-year grant, which runs from August 15, 2023 to July 31, 2026, aims to create a practical and generic tool for fitting simulation models to real-world data across diverse domains like astronomy, ecology, climate science, and...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $227,251 Project Grant to Florida State University to develop novel statistical methodology for scalable Bayesian inference without Markov Chain Monte Carlo (MCMC) techniques. The research aims to create a generalized conjugate multivariate (GCM) distribution framework that enables direct sampling from exact posterior distributions, particularly for high-dimensional spatio-temporal data prevalent across...
This $375,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to advance Bayesian inference methods for computational models in the behavioral sciences. The 3-year project, awarded to Rensselaer Polytechnic Institute, aims to develop a framework for efficient Bayesian inference that enables researchers to quickly fit, criticize, and adapt complex mechanistic models. The research will make...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
This Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...
This National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, awarded to Carnegie Mellon University, provides $240,000 in funding from September 1, 2023 to August 31, 2026. The grant supports research to advance statistical predictive inference methods, addressing challenges in areas like cross-validation, high-dimensional statistical comparisons, and conformal prediction. The project aims to develop novel techniques with strong mathematical justifications that can...
This $131,615 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences, under the CFDA program 47.049 Mathematical and Physical Sciences, aims to develop novel Bayesian statistical models for analyzing complex high-dimensional health data. The research will focus on creating improved joint models that can leverage information from longitudinal measurements, such as clinical data and biomarkers, to better predict time-to-event outcomes like disease...
This $299,669 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of new flexible Bayesian nonparametric statistical methods. The research team at the University of Wisconsin - Madison aims to create innovative Bayesian inference approaches that can account for departures from parametric generative models, while maintaining the interpretability of traditional Bayesian modeling. The project will...