A Rigorous Statistical Framework for the Mathematics of Sensing, Exploitation and Execution

Abstract

A research program has been performed to develop next-generation mathematics for sensing, exploitation and execution (MSEE). An important focus of the research has been on a new class of nonparametric Bayesian architectures that constitute a rich modeling framework while still yielding parsimonious representations. Such models are attractive from multiple perspectives: (i) they flexibly adjust model complexity and sophistication to match the observed data, while (ii) explicitly defining model uncertainty manifested by missing data, and thereby (iii) linking utility of data to the objectives and associated models; additionally, (iv) these models are ideal for joint modeling of heterogeneous and possibly contradictory data, by sharing an inferred and typically low-dimensional latent space. In the MSEE construct, the utility of data is linked to the sensing objective, which in turn motivates and refines the associated models.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2015
Accession Number
ADA625376

Entities

People

  • Lawrence Carin

Organizations

  • Duke University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Bayesian Networks
  • Data Mining
  • Data Science
  • Deep Belief Networks
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Monte Carlo Method
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Sigmoid Belief Networks
  • Statistical Algorithms

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • Space