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On the Bayesian Analysis of Ring‐Recovery Data
Author(s) -
Brooks S. P.,
Catchpole E. A.,
Morgan B. J. T.,
Barry S. C.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00951.x
Subject(s) - bayesian probability , biometrics , statistics , bayesian statistics , prior probability , estimator , computer science , bayesian linear regression , posterior probability , maximum likelihood , bayesian average , ridge , sensitivity (control systems) , bayesian inference , econometrics , mathematics , artificial intelligence , geology , paleontology , electronic engineering , engineering
Summary. Vounatsou and Smith (1995, Biometrics 51 , 687–708) describe the modern Bayesian analysis of ring‐recovery data. Here we discuss and extend their work. We draw different conclusions from two major data analyses. We emphasize the extreme sensitivity of certain parameter estimates to the choice of prior distribution and conclude that naive use of Bayesian methods in this area can be misleading. Additionally, we explain the discrepancy between the Bayesian and classical analyses when the likelihood surface has a flat ridge. In this case, when there is no unique maximum likelihood estimate, the Bayesian estimators are remarkably precise.