Applied measure theory for probabilistic modeling.
Paper i 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
Författare
Chad Scherrer
Informative Prior
Moritz Schauer
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
JuliaCon Proceedings
Vol. 2022 1
Virtual; online, ,
Ämneskategorier
Sannolikhetsteori och statistik
DOI
10.21105/jcon.00092