Bayesian Software Prediction Models. Volume II. Classical and Bayesian Inference for the Software Imperfect Debugging Model.

Abstract

This report presents two methods for statistical inference of the parameters of the imperfect debugging model proposed by Goel and Okumoto. Using the method of maximum likelihood, the mle's, the likelihood contours and the confidence regions for N, p and lambda are obtained. A Bayesian approach is presented to obtain the Bayesian point estimates and the H.P.D. regions. Numerical examples based on simulated data are used for illustrative purposes. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1978
Accession Number
ADA057871

Entities

People

  • Amrit L. Goel
  • K. Okumoto

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Air Platforms
  • C4I

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Inference
  • Bayesian Networks
  • Coefficients
  • Command And Control
  • Computational Science
  • Confidence Limits
  • Data Sets
  • Debugging
  • Maximum Likelihood Estimation
  • Operations Research
  • Probability
  • Random Variables
  • Reliability
  • Simulations
  • Software Development
  • Statistical Inference

Fields of Study

  • Mathematics

Readers

  • Computer Science.
  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference