Applied measure theory for probabilistic modeling.
Paper in proceeding, 2022
foundations of probability together with powerful combinators and transforms, giving a gentle introduction to the concepts in this article.
The task is foremost achieved by recognizing measure as the central object. This enables us to develop a proper concept of densities as objects relating measures with each others. As densities provide local perspective on measures, they are the key to efficient implementations.
The need to preserve this computationally so important locality leads to the new notion of locally-dominated measure, solving the so-called “base measure problem” and making work with densities and distributions in Julia easier and more flexible.
Julia, Measure theory, Probability
Author
Chad Scherrer
Informative Prior
Moritz Schauer
University of Gothenburg
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
JuliaCon Proceedings
Vol. 2022 1
Virtual; online, ,
Subject Categories
Probability Theory and Statistics
DOI
10.21105/jcon.00092