New Algorithms for Nonlinear Least Squares and Bayesian Parameter Estimation.

Abstract

Some new algorithms are presented for fitting mathematical models to multiple-response experiments. These algorithms give estimates of the parameters in a user-defined predictor model, and also estimate the parameters of a Gaussian model of the observational error distribution. The development is based on Bayes' theorem, and provides a natural extension of known least-squares estimation methods. Allowance is made for missing values of responses, which occur frequently in practical work.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1980
Accession Number
ADA083819

Entities

People

  • Jan P. Sorensen
  • Warren E. Stewart

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Contracts
  • Covariance
  • Data Science
  • Differential Equations
  • Equations
  • Intervals
  • Mathematical Models
  • Mathematics
  • Normal Distribution
  • Residuals
  • Schools
  • Theorems
  • United States
  • Universities
  • Wisconsin

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

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