Fitting genetic models using Markov Chain Monte Carlo algorithms with BUGS
Maximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene-environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of... Mehr ...
Verfasser: | |
---|---|
Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2006 |
Reihe/Periodikum: | van den Berg , S M , Beem , A L & Boomsma , D I 2006 , ' Fitting genetic models using Markov Chain Monte Carlo algorithms with BUGS ' , Twin Research and Human Genetics , vol. 9 , no. 3 , pp. 334-342 . https://doi.org/10.1375/183242706777591399 |
Schlagwörter: | /dk/atira/pure/keywords/cohort_studies/netherlands_twin_register_ntr_ / name=Netherlands Twin Register (NTR) / /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being / name=SDG 3 - Good Health and Well-being |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-28798803 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://research.vu.nl/en/publications/7ba86727-7320-433f-a155-11379f8610d7 |