Accession Number : AD1047179


Descriptive Note : Technical Report,01 Oct 2013,01 Dec 2017

Corporate Author : Indiana University Bloomington United States

Personal Author(s) : Carett,Jacques ; Kiselyov,Oleg

Full Text :

Report Date : 15 Feb 2018

Pagination or Media Count : 16

Abstract : We address the problem that probabilistic inference algorithms are dicult and tedious to implement, by expressing them in terms of a small number of building blocks, which are automatic transformations on probabilistic programs. On one hand, our curation of these building blocks reflects the way human practitioners discuss probabilistic inference with each other, so our probabilistic programming language supports modular composition of inference procedures and serves as a medium for collaboration. On the other hand, our implementation of these building blocks combines high-level mathematical reasoning with low-level computational optimization, so the speed and accuracy of the generated solvers are competitive with state-of-the-art systems.

Distribution Statement : APPROVED FOR PUBLIC RELEASE