Last edited by Goltinos
Sunday, May 10, 2020 | History

2 edition of Bayesian statistics in auditing found in the catalog.

Bayesian statistics in auditing

Michael A. Crosby

Bayesian statistics in auditing

a comparison of probability elicitation techniques

by Michael A. Crosby

  • 142 Want to read
  • 29 Currently reading

Published by Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University in West Lafayette, Ind .
Written in English

    Subjects:
  • Auditing -- Statistical methods.,
  • Bayesian statistical decision theory.

  • Edition Notes

    Bibliography: p. 23-24.

    Statementby Michael A. Crosby.
    SeriesPaper - Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management ;, no. 702, Paper (Krannert Graduate School of Management. Institute for Research in the Behavioral, Economic, and Management Sciences) ;, no. 702.
    Classifications
    LC ClassificationsHD6483 .P8 no. 702, HF5667 .P8 no. 702
    The Physical Object
    Pagination25, 7 p. ;
    Number of Pages25
    ID Numbers
    Open LibraryOL4459520M
    LC Control Number79122708

      Medical Book Introduction to Bayesian Statistics, 2nd Edition —Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics.". This work applies Bayesian statistical techniques to the management of risk in auditing procedures. Areas covered include audit judgements, the equivalent prior sample, substantive testing and .

    Chapter 1 The Basics of Bayesian Statistics. Bayesian statistics mostly involves conditional probability, which is the the probability of an event A given event B, and it can be calculated using the Bayes rule. The concept of conditional probability is widely used in medical testing, in which false positives and false negatives may occur. I was invited by the Statistics Department at the Oregon State University to give ten lectures on Bayesian Statistics in July This monograph is a slightly expanded version of the content of those lectures. An adherent of the school of subjective probability might be forgiven for presenting a .

    The difference between Bayesian and frequentist inference in a nutshell: With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process happened to be), to quantify evidence for every possible value of θ. With frequentism, you make assumptions about the process that generated your data and.   The way that Bayesian probability is used in corporate America is dependent on a degree of belief rather than historical frequencies of identical or similar events. The model is versatile, : Daniel Mcnulty.


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