Bayesian Inference for Ordinal Data Using Multivariate Probit Models
نویسندگان
چکیده
Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient methodology for Bayesian inference for multivariate probit models using Markov chain Monte Carlo techniques. The key idea for our approach is the novel use of parameter expansion to sample correlation matrices. We also propose methodology for model selection. Our approach is demonstrated through several real and simulated examples.
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