Bayesian Analysis

Nonparametric Bayesian models through probit stick-breaking processes

David B. Dunson and Abel Rodríguez

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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.

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Bayesian Anal. Volume 6, Number 1 (2011), 145-177.

First available in Project Euclid: 13 June 2012

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Zentralblatt MATH identifier

Primary: 62F15: Bayesian inference
Secondary: 60G57: Random measures 62G99: None of the above, but in this section 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62M30: Spatial processes 62P12: Applications to environmental and related topics

Nonparametric Bayes Random Probability Measure Stick-breaking Prior Mixture Model Data Augmentation Spatial Data Time Series


Rodríguez, Abel; Dunson, David B. Nonparametric Bayesian models through probit stick-breaking processes. Bayesian Anal. 6 (2011), no. 1, 145--177. doi:10.1214/11-BA605.

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