PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An efficient sequential Monte Carlo algorithm for coalescent clustering
Dilan Gorur and Yee Whye Teh
In: NIPS 2008, 8-11 Dec 2008, Vancouver, Canada.


We propose an efficient sequential Monte Carlo inference scheme for the recently proposed coalescent clustering model [1]. Our algorithm has a quadratic runtime while those in [1] is cubic. In experiments, we were surprised to find that in addition to being more efficient, it is also a better sequential Monte Carlo sampler than the best in [1], when measured in terms of variance of estimated likelihood and effective sample size.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4859
Deposited By:Dilan Gorur
Deposited On:24 March 2009