2015/01/31 by Abdolreza Mohammadi, Fentaw Abegaz, Edwin R. van den Heuvel +2 · 1 citation
Mathematics · Medicine · #Artificial intelligence #Bayesian probability #Computer science #Conditional independence #Copula (linguistics) #Data mining #Dupuytren's Contracture and Treatments #Econometrics #Graphical model #Machine learning #Markov chain Monte Carlo #Mathematics #Multivariate statistics #stat.AP
paper · pdf · doi:10.1111/rssc.12171
arxiv created 2015/09/15 · openalex publication_date 2016/09/01 · arxiv updated 2017/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Summary Dupuytren disease is a fibroproliferative disorder with unknown aetiology that often progresses and eventually can cause permanent contractures of the fingers affected. We provide a computationally efficient Bayesian framework to discover potential risk factors and investigate which fingers are jointly affected. Our Bayesian approach is based on Gaussian copula graphical models, which provide a way to discover the underlying conditional independence structure of variables in multivariate data of mixed types. In particular, we combine the semiparametric Gaussian copula with extended rank likelihood to analyse multivariate data of mixed types with arbitrary marginal distributions. For structural learning, we construct a computationally efficient search algorithm by using a transdimensional Markov chain Monte Carlo algorithm based on a birth–death process. In addition, to make our statistical method easily accessible to other researchers, we have implemented our method in C++ and provide an interface with R software as an R package BDgraph, which is freely available from http://CRAN.R-project.org/package=BDgraph.