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TreeStructured Stick Breaking for Hierarchical Data Ryan Prescott Adams #

Summary: Tree­Structured Stick Breaking for Hierarchical Data
Ryan Prescott Adams #
Dept. of Computer Science
University of Toronto
Zoubin Ghahramani
Dept. of Engineering
University of Cambridge
Michael I. Jordan
Depts. of EECS and Statistics
University of California, Berkeley
Many data are naturally modeled by an unobserved hierarchical structure. In this
paper we propose a flexible nonparametric prior over unknown data hierarchies.
The approach uses nested stick­breaking processes to allow for trees of unbounded
width and depth, where data can live at any node and are infinitely exchangeable.
One can view our model as providing infinite mixtures where the components
have a dependency structure corresponding to an evolutionary diffusion down a
tree. By using a stick­breaking approach, we can apply Markov chain Monte Carlo
methods based on slice sampling to perform Bayesian inference and simulate from
the posterior distribution on trees. We apply our method to hierarchical clustering


Source: Adams, Ryan Prescott - Department of Electrical and Computer Engineering, University of Toronto


Collections: Computer Technologies and Information Sciences