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, 08 Dec - 13 Dec 2008, Vancouver, Canada.


We propose an efficient sequential Monte Carlo inference scheme for the recently proposed coalescent clustering model (Teh et al, 2008). Our algorithm has a quadratic runtime while those in (Teh et al, 2008) 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 (Teh et al, 2008), when measured in terms of variance of estimated likelihood and effective sample size.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5107
Deposited By:Yee Whye Teh
Deposited On:24 March 2009