Bayesian Models for Response Surfaces of Uncertain Functional Form.

Abstract

Experimental response functions are often approximated by simple empirical functions such as polynomials. Several methods for modeling such responses which take into account this approximate nature are described and are shown to be essentially equivalent. The models all involve a Bayesian analysis which reflects prior experimnetal belief about the ability of the empirical approximation to represent the true response function. The models are also related to Kalman filters. Implications of the models for statistical inference are examined with particular attention to estimating the response function. Numerical examples help illustrate the models. A general predictive check is developed to examine the consistency of model with the observed data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1983
Accession Number
ADA127703

Entities

People

  • David M. Steinberg

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Data Science
  • Estimators
  • Experimental Data
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Numerical Analysis
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Stochastic Processes
  • United States

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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