Random Variate Generation for Bayesian Nonparametric Reliability Analysis

Abstract

Simulation modeling requires accurate input analysis to ensure validity of the study. Hence, the mantra "garbage in = garbage out." Much of the research and simulation code that has been written to date has been focused on traditional parametric methods. Here we investigate Bayesian nonparametric methods for input modeling and reliability analysis. Bayesian nonparametric methods have been shown in many cases to produce better predictive models. Also, for use in a Bayesian setting, we have written C++ classes for random variate generation. These contain functions for standard and truncated distributions as well as functions for statistical data handling. Although we have written the code for Bayesian algorithms, the functions can be used anywhere a good source of random variates is needed. Included is a detailed description of class implementation and usage along with complete source code.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2005
Accession Number
ADA435110

Entities

People

  • Patrick J. Munson

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Science
  • Computer Programs
  • Data Science
  • Distribution Functions
  • Information Processing
  • Information Science
  • Probability
  • Probability Distributions
  • Random Variables
  • Reliability
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Data
  • Stochastic Processes
  • Surveys

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference