A Stick-Breaking Construction of the Beta Process
John William Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence Carin · International Conference on Machine Learning · 2010
We present and derive a new stick-breaking construction of the beta process. The construction is closely related to a special case of the stick-breaking construction of the Dirichlet process (Sethuraman, 1994) applied to the beta distribution. We derive an inference procedure that relies on Monte Carlo integration to reduce the number of parameters to be inferred, and present results on synthetic data, the MNIST handwritten digits data set and a time-evolving gene expression data set.