Applications of the EM Algorithm to the Estimation of Bayesian Hyperparameters.

Abstract

Applications of the EM algorithm to the estimation of Bayesian hyperparameters are discussed and reviewed in the context of the author's philosophy involving the inductive and pragmatic modelling of sampling distributions and prior structures. Frequently the hyperparameters may be estimated from the data, thus avoiding the subjective assessment of these values. The ideas are applied to multiple regression models, histograms and multinomial distributions. A numerical example is described in the context of smoothing the cell probabilities of several multinomial distributions. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1982
Accession Number
ADA114537

Entities

People

  • Tom Leonard

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Contracts
  • Data Analysis
  • Data Science
  • Estimators
  • Histograms
  • Information Science
  • Mathematics
  • Philosophy
  • Probability
  • Probability Distributions
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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