title: Nonparametric Bayesian models through probit stick-breaking processes.
abstract: We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.
authors.affiliation: Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA.
authors.forename: Abel
authors.initials: A
authors.lastname: Rodríguez
authors.affiliation: Department of Statistics, Duke University, Durham, NC.
authors.forename: David B
authors.initials: DB
authors.lastname: Dunson
keywords: Data Augmentation; Mixture Model; Nonparametric Bayes; Random Probability Measure; Spatial Data; Stick-breaking Prior; Time Series