Accession Number:



The Next Generation of Probabilistic Programming: Massive Data, Data Systems, and Model Diagnostics

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

Princeton University

Personal Author(s):

Report Date:


Pagination or Media Count:



This effort made significant progress on inference for probabilistic programming. Probabilistic programming requires inference methods for approximating conditional distributions. Building on the framework of variational inference, this effort made this algorithm more efficient, more powerful, and more accurate. This effort developed new probabilistic models for economics, neuroscience, text analysis, population genetics, social network analysis, and recommendation systems. These methods were deployed in open-source software, on real-world programming systems and are currently in use by end-users of probabilistic programming. The work performed under this effort changed the landscape of approximate posterior inference, pushing forward the field of Bayesian machine learning and probabilistic programming.

Subject Categories:

  • Computer Programming and Software

Distribution Statement:

[A, Approved For Public Release]