Augment-and-conquer negative binomial processes
Abstract
By developing data augmentation methods unique to the negative binomial (NB) distribution,
we unite seemingly disjoint count and mixture models under the NB process framework.
We develop fundamental properties of the models and derive efficient Gibbs sampling
inference. We show that the gamma-NB process can be reduced to the hierarchical Dirichlet
process with normalization, highlighting its unique theoretical, structural and computational
advantages. A variety of NB processes with distinct sharing mechanisms are constructed
and applied to topic modeling, with connections to existing algorithms, showing the
importance of inferring both the NB dispersion and probability parameters.
Type
Journal articlePermalink
https://hdl.handle.net/10161/8950Collections
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Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011

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