Illustrating Frequentist and Bayesian Statistics in Oceanography,

Abstract

Both frequentist and Bayesian methodologies provide means for a statistical solution to a problem. However, it is usually the case that, for a given situation, one methodology is more appropriate. Using a number of oceanographic examples we explore the components of a statistical solution and illustrate the most appropriate methodology. We argue that the statistical consideration of utmost importance is the type of inference and conclusion to be made. In some examples it is more appropriate to make this inference as a Bayesian, and in some it is more appropriate to make this inference as a frequentist.

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1993
Accession Number
ADP008736

Entities

People

  • George Casella

Organizations

  • Cornell University

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Earth Sciences
  • Information Science
  • Interdisciplinary Science
  • Mathematics
  • Oceanography
  • Physical Oceanography
  • Statistics
  • Workshops

Fields of Study

  • Mathematics

Readers

  • Educational Psychology
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference