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

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2019
Accession Number
AD1067306

Entities

People

  • David M. Biel

Organizations

  • Princeton University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computational Science
  • Computer Programming
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Set
  • Digital Data
  • Factor Analysis
  • Genetic Variation
  • Genetics
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Models
  • Monte Carlo Method
  • Network Science
  • Neural Networks
  • Population Genetics
  • Probabilistic Models
  • Probability
  • Random Variables
  • Social Networks

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Software Engineering.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology