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This research aims to develop a foundational framework for probabilistic programming, focusing on Bayesian statistical modeling. It seeks to create new programming languages and inference methods that clarify complex statistical models and their symmetries.
Probabilistic programming is a powerful method for Bayesian statistical modelling, particularly where the sample space is complex or unbounded (non-parametric).
This is because the statistical model can be described clearly in a way that is precise but separate from inference algorithms.
It accommodates complex models in such a way that outcomes are still explainable.The objective of the proposed research is to develop a semantic foundation for probabilistic programming that properly explains th…
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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