Sampling and Bayes' Inference in Scientific Modeling and Robustness.

Abstract

Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complimentary parts - a predictive part allowing model criticism, and a Bayes posterior part allowing estimation. Implications for significance tests, the theory of precise measurement, and for ridge estimates are considered. Predictive checking functions for transformation, serial correlation, bad values, and their relation with Bayesian options are considered. Robustness is seen from a Bayesian viewpoint and examples are given. For the bad value problem a comparison with M estimators is made. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1980
Accession Number
ADA096645

Entities

People

  • George E. P. Box

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Acquisition
  • Bayesian Inference
  • Computational Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Estimators
  • Information Processing
  • Information Science
  • Mathematics
  • Probability
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Surveys
  • United States

Readers

  • Distributed Systems and Data Platform Development
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference