Bayesian Modeling of Inverse Gaussian Outcomes in a Randomized Design

This undergraduate project explored Bayesian modeling strategies for inverse Gaussian outcomes under a completely randomized design.

The work emphasized interpretable parameterization choices and practical prior specification to improve uncertainty quantification.

This page currently includes only a short background summary; detailed derivations and implementation notes may be added later.