Cloud Library for Directed Probabilistic Graphical Models

Abstract

The project aimed to build a massively parallel library for Bayesian networks by creating a data analytical capability with potential throughput commensurate with DoD data volumes. The goal was to implement data-parallel independent & identically distributed inference & learning in Bayesian networks & accomplish nearly-linear scaling. They re-examined & implemented data structures & algorithms needed for distributed-model inference. The inference aimed at being able to ask & answer privacy & adversarial learning questions where model distribution is due to private nature of the data. They looked for efficiently-parallelizable methods of inference & learning.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2014
Accession Number
ADA611690

Entities

People

  • Haiqin Wang
  • Marek J. Druzdzel

Organizations

  • Boeing

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Bayesian Networks
  • Computations
  • Computer Programs
  • Data Mining
  • Data Sets
  • Department Of Defense
  • Government Procurement
  • Governments
  • Learning
  • Machine Learning
  • Models
  • Probabilistic Models
  • Social Media
  • Xml

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks