Computer Assisted Improvement of the Estimation Mean Squared Error with Application to Back Propagation Neural Networks.

Abstract

A computer assisted method for improving the mean squared error (MSE) in estimation for parametric models is presented. Assuming existence of nontrivial sufficient statistics, the method involves generation of Monte Carlo samples from the conditional distribution of the observables, given the sufficient statistic(s). The method is illustrated in connection with a simple back-propagation neural network model for estimating a logistic regression function. Key Words and Phrases: Parametric estimation, exponential families, nonlinear models, nonlinear least squares, neural networks, Monte Carlo simulation, computer intensive statistical methods.

Document Details

Document Type
Technical Report
Publication Date
Jul 24, 1991
Accession Number
ADA247724

Entities

People

  • J. E. Angus

Organizations

  • Naval Health Research Center

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computers
  • Data Science
  • Information Science
  • Monte Carlo Method
  • Neural Networks
  • Nonlinear Dynamics
  • Simulations
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks