Computational Methodologies for the Post-Bayesian Paradigm

This thesis develops new methods to help prediction models express how certain they are about their results. In fields like medicine and finance, relying on a single answer can be dangerous and costly. This research creates faster and more flexible tools for calculating these confidence levels. These improvements allow experts to identify when a model is likely to be wrong. Practically, this work makes computer-driven decisions more reliable and safer for high-stakes applications in the real world.