On the One Arm Bandit Problem.

Abstract

The author considers the one arm bandit problem when future losses are discounted by a factor gamma per time period, 0 < gamma < 1. The author adopts a Bayesian approach and formulates the problem as an optimal stopping problem. In the case of a uniform distribution with an unknown range or normal distribution with an unknown mean, it is shown that the boundaries of the continuation region in the parameter space of the conjugate prior family approach limits as gamma nears 1 (after appropriate normalization in the normal case). (Modified author abstract)

Document Details

Document Type
Technical Report
Publication Date
Jul 29, 1974
Accession Number
AD0783685

Entities

People

  • Michael Woodroofe

Organizations

  • George Washington University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Bayesian Networks
  • Boundaries
  • Distribution Functions
  • Functions (Mathematics)
  • Mathematical Models
  • Mathematics
  • Normal Distribution

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.

Technology Areas

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