Bayesian non-parametric analysis of multirater ordinal data, with application to prioritizing research goals for prevention of suicide

Our application data are produced from a scalable, on-line expert elicitation process that incorporates hundreds of participating raters to score the importance of research goals for the prevention of suicide with the purpose of informing policy making. We develop a Bayesian formulation for analysis of ordinal multirater data motivated by our application. Our model employs a non-parametric mixture distribution over rater-indexed parameters for a latent continuous response under a Poisson-Dirichlet process mixing measure that allows inference about distinct rater behavioural and learning typologies from realized clusters.