This $299,669 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will enable researchers at Texas A&M University to develop flexible Bayesian non-parametric statistical modeling approaches. The primary objectives are to create generative models that allow flexible departures from traditional parametric forms, while retaining the interpretability of parametric Bayesian modeling. This research aims to provide more robust and user-friendly statistical methods for scientific learning problems in fields like neuroscience and nuclear physics, where existing models may have limitations. The award recipient will also focus on enhancing the pedagogy of these techniques through student advising and course development. The project is scheduled to be completed by June 30, 2027.
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