Nonparametric Bayesian models through probit stick-breaking processes.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Rodríguez, A; Dunson, DB

Published Date

  • March 2011

Published In

Volume / Issue

  • 6 / 1

PubMed ID

  • 24358072

Pubmed Central ID

  • 24358072

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/11-ba605

Language

  • eng