Checking Convergence to Posterior Distribution

Dana Kelly () and Curtis Smith ()
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Dana Kelly: Idaho National Laboratory (INL)
Curtis Smith: Idaho National Laboratory (INL)

Chapter Chapter 6 in Bayesian Inference for Probabilistic Risk Assessment, 2011, pp 61-65 from Springer

Abstract: Abstract One issue with any Monte Carlo sampling technique, and especially Markov chain Monte Carlo, is convergence. Before samples can be used for parameter estimation, the analyst must have reasonable assurance that the Markov chain(s) used to generate the samples has converged to the posterior distribution. This chapter presents qualitative and quantitative convergence checks that an analyst can use to obtain this assurance and avoid pitfalls caused by lack of convergence.

Keywords: Posterior Distribution; Markov Chain Monte Carlo; Good Coverage; Monte Carlo Sampling; Markov Chain Monte Carlo Sample (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-84996-187-5_6

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DOI: 10.1007/978-1-84996-187-5_6

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