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Familial tendency to foetal loss analysed with Bayesian graphical models by Gibbs sampling
Author(s) -
Hundborg Heidi H.,
Højbjerre Malene,
Bjarne Christiansen Ole,
Lauritzen Steffen L.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/1097-0258(20000830)19:16<2147::aid-sim498>3.0.co;2-c
Subject(s) - gibbs sampling , markov chain monte carlo , bayesian probability , graphical model , computer science , markov chain , sampling (signal processing) , econometrics , statistics , machine learning , artificial intelligence , mathematics , filter (signal processing) , computer vision
This paper presents several models for investigating whether the HLA allogenotypes DR1/Br, DR3 and DR10 are genetic markers for a predisposition of experiencing unexplained recurrent foetal losses. A total of 199 women from 113 families answered questionnaires concerning their pregnancies and 145 of these women were HLA typed. The analysis of the data is complicated as dependencies between pregnancy outcomes are expected. The main purpose of the paper is to illustrate how such analyses can be performed using Bayesian graphical models and Gibbs sampling. The analyses are made using the programs BUGS and CODA. Markov chain Monte Carlo analyses within a Bayesian framework have become easier with the introduction of these programs. However, experience shows that some caution is required so we recommend making some initial analyses using very simple models and perhaps approximative methods, followed by a model development introducing increasing complexity. Copyright © 2000 John Wiley & Sons, Ltd.