Nonparametric Bayesian models through probit stick-breaking processes.

Journal Article (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

Published version (via Digital Object Identifier)
Pubmed Central version

Duke Authors

Dunson, David B.

Cited Authors

Rodríguez, A; Dunson, DB

Published Date

March 2011

Published In

Bayesian Analysis

Volume / Issue

6 / 1

PubMed ID

24358072

Pubmed Central ID

PMC3865248

Electronic International Standard Serial Number (EISSN)

1931-6690

International Standard Serial Number (ISSN)

1936-0975

Digital Object Identifier (DOI)

10.1214/11-ba605

Scholars@Duke