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Bayesian Modeling of Population Variability--Practical Guidance and Pitfalls. PSAM -9 (Preprint).


DE2008933190

Publication Date 2008
Personal Author Kelly, D. L.; Atwood, C. L.
Page Count 10
Abstract With the advent of easy-to-use open-source software for Markov chain Monte Carlo (MCMC) simulation, hierarchical Bayesian analysis is gaining in popularity. This paper presents practical guidance for hierarchical Bayes analysis of typical problems in probabilistic safety assessment (PSA). The guidance is related to choosing parameterizations that accelerate convergence of the MCMC sampling and to illustrating the potential sensitivity of the results to the functional form chosen for the first-stage prior. This latter issue has significant ramifications because the mean of the average population variability curve (PVC) from hierarchical Bayes (or the mean of the point estimate distribution from empirical Bayes) can be very sensitive to this choice in cases where variability is large. Numerical examples are provided to illustrate the issues discussed.
Keywords
  • Probabilistic estimation
  • Risk assessment
  • Sensitivity
  • Chains
  • Convergence
  • Distribution
  • Functionals
  • Simulation
  • Bayes theorem
Source Agency
  • Technical Information Center Oak Ridge Tennessee
Corporate Authors Idaho National Laboratory, Idaho Falls, ID.; Department of Energy, Washington, DC.
Supplemental Notes Sponsored by Department of Energy, Washington, DC.
Document Type Technical Report
NTIS Issue Number 200901
Bayesian Modeling of Population Variability--Practical Guidance and Pitfalls. PSAM -9 (Preprint).
Bayesian Modeling of Population Variability--Practical Guidance and Pitfalls. PSAM -9 (Preprint).
DE2008933190

  • Probabilistic estimation
  • Risk assessment
  • Sensitivity
  • Chains
  • Convergence
  • Distribution
  • Functionals
  • Simulation
  • Bayes theorem
  • Technical Information Center Oak Ridge Tennessee
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