Nonparametric Bayesian models through probit stick-breaking processes.
Publication
, Journal Article
Rodríguez, A; Dunson, DB
Published in: Bayesian analysis
March 2011
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.
Duke Scholars
Published In
Bayesian analysis
DOI
EISSN
1931-6690
ISSN
1936-0975
Publication Date
March 2011
Volume
6
Issue
1
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics
Citation
APA
Chicago
ICMJE
MLA
NLM
Rodríguez, A., & Dunson, D. B. (2011). Nonparametric Bayesian models through probit stick-breaking processes. Bayesian Analysis, 6(1). https://doi.org/10.1214/11-ba605
Rodríguez, Abel, and David B. Dunson. “Nonparametric Bayesian models through probit stick-breaking processes.” Bayesian Analysis 6, no. 1 (March 2011). https://doi.org/10.1214/11-ba605.
Rodríguez A, Dunson DB. Nonparametric Bayesian models through probit stick-breaking processes. Bayesian analysis. 2011 Mar;6(1).
Rodríguez, Abel, and David B. Dunson. “Nonparametric Bayesian models through probit stick-breaking processes.” Bayesian Analysis, vol. 6, no. 1, Mar. 2011. Epmc, doi:10.1214/11-ba605.
Rodríguez A, Dunson DB. Nonparametric Bayesian models through probit stick-breaking processes. Bayesian analysis. 2011 Mar;6(1).
Published In
Bayesian analysis
DOI
EISSN
1931-6690
ISSN
1936-0975
Publication Date
March 2011
Volume
6
Issue
1
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics