Bayesian Nonparametric Prediction and Statistical Inference

Abstract

The problem of Bayesian nonparametric prediction and statistical inference is formulated and discussed. A solution is proposed based upon A sub n and H sub n as in Hill (1968). The meaning of parameters in the subjective Bayesian theory of Bruno de Finetti is discussed in connection both with A sub n and with conventional parametric models. It is argued that the usual sharp distinction between prediction and parametric inference is largely illusory. The finite version of de Finetti's theorem is emphasized for the practice of statistics, with the infinite case used only to obtain approximations and insight. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Sep 07, 1989
Accession Number
ADA218473

Entities

People

  • Bruce M. Hill

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Cyber
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Inference
  • Bayesian Networks
  • Data Analysis
  • Data Mining
  • Data Science
  • Distribution Functions
  • Information Science
  • Mathematics
  • Order Statistics
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Inference
  • Statistical Samples
  • Statistics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

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