Applied Measure Theory for Probabilistic Modeling

نویسندگان

چکیده

Probabilistic programming and statistical computing are vibrant areas in the development of Julia language, but underlying infrastructure dramatically predates recent developments. The goal MeasureTheory.jl is to provide with right vocabulary tools for these tasks. In package we introduce a well-chosen set notions from foundations probability together powerful combinators transforms, giving gentle introduction concepts this article. task foremost achieved by recognizing measure as central object. This enables us develop proper concept densities objects relating measures each others. As local perspective on measures, they key efficient implementations. need preserve computationally so important locality leads new notion locally-dominated solving so-called base problem making work distributions easier more flexible.

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ژورنال

عنوان ژورنال: Proceedings of the JuliaCon conferences

سال: 2022

ISSN: ['2642-4029']

DOI: https://doi.org/10.21105/jcon.00092