Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling

Abstract

This was a short-term proof-of-concept project with the goal of demonstrating the feasibility of, and lay the theoretical foundations for, integration of predictive neural networks into Bayesian networks as a means of generating probability distribution functions and likelihood tables. The challenges were two-fold: first, developing a way to convert XY data output from an instrument to a probability density functionusing a neural network and secondly, fusing this and other types of sensor output into a single probabilistic evaluation of multiple sensor outputs. Ultimately, this would be useful in application such as networked sensor arrays such as might be deployed to detect chemical agentsin a subway system for example.

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Document Details

Document Type
Technical Report
Publication Date
May 31, 2016
Accession Number
AD1024898

Entities

People

  • Suzanne Bell

Organizations

  • West Virginia University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Chemistry
  • Data Fusion
  • Department Of Defense
  • Distribution Functions
  • Engineering
  • Information Science
  • Neural Networks
  • Predictive Modeling
  • Probability
  • Probability Density Functions
  • Probability Distribution Functions
  • Probability Distributions
  • Standards
  • Statistical Analysis
  • Students

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks