On Multiple Spline Approximations for Bayesian Computations

Abstract

Probabilistic reasoning typically suffers from the explosive amount of information it must maintain. There are a variety of methods available for curbing this explosion. However, in doing so, it is important to avoid oversimplifying the given domain through injudicious use of assumptions such as independence. Multiple splining is an approach for compressing and approximating the probabilistic information. Instead of positing additional independence conditions, it attempts to identify patterns in the information.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA529519

Entities

People

  • Eugene Santos

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Bayesian Networks
  • Computations
  • Computer Science
  • Detectors
  • Integer Programming
  • Linear Programming
  • Mathematics
  • Notation
  • Operations Research
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Real Variables
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Artificial Intelligence

Technology Areas

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