Gaussian beta process

dc.contributor.advisor

Dunson, David B

dc.contributor.author

Wang, Yingjian

dc.date.accessioned

2015-01-28T18:11:11Z

dc.date.available

2016-01-21T05:30:05Z

dc.date.issued

2014

dc.department

Statistical Science

dc.description.abstract

This thesis presents a new framework for constituting a group of dependent completely random measures, unifying and extending methods in the literature. The dependent completely random measures are constructed based on a shared completely random measure, which is extended to the covariate space, and further differentiated by the covariate information associated with the data for which the completely random measures serve as priors. As a concrete example of the flexibility provided by the framework, a group of dependent feature learning measures are constructed based on a shared beta process, with Gaussian processes applied to build adaptive dependencies learnt from the practical data, denoted as the Gaussian beta process. Experiment results are presented for gene-expression series data (time as covariate), as well as digital image data (spatial location as covariate).

dc.identifier.uri

https://hdl.handle.net/10161/9447

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Statistics

dc.subject

Engineering

dc.subject

Machine Learning

dc.subject

Stochastic Process

dc.title

Gaussian beta process

dc.type

Master's thesis

duke.embargo.months

12

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