Discretizing a Normal Prior for Change Point Estimation in Switching Regressions

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2 Scopus citations


Bayes decision procedures are considered for change point estimation in the simple bilinear segmented model. A discretized normal prior density is employed as the prior distribution for the change point index. Posterior probability functions are developed for this index under a vague prior formulation on the regression parameters. The procedure is applied to an example involving mercury toxicity data.

Original languageEnglish (US)
Pages (from-to)777-782
Number of pages6
JournalBiometrical Journal
Issue number7
StatePublished - 1987


  • Bayes analysis
  • Nonlinear regression
  • Segmented regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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