Further Theory of Stable Decisions.

Abstract

In a decision problem, a Bayesian has a loss function, a likelihood function, and a prior opinion. A Bayesian makes a decision by minimizing expected loss. Assuming the likelihood function is fixed and agreed upon, this thesis examines the influence of small variations in loss function and prior opinion. There are a lot of ways to define small variations in loss function and prior distribution. Essentially one has to define distance between two distributions and distance between two loss functions.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA060026

Entities

People

  • David T. Chuang

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Continuity
  • Convergence
  • Data Science
  • Distribution Functions
  • Indicators
  • Inequalities
  • Information Science
  • Materials
  • Military Research
  • Normal Distribution
  • Notation
  • Probability
  • Sequences
  • Statistics
  • Universities
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Space/Atmospheric Physics.
  • Strategic Security Studies

Technology Areas

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