An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

Authors

Dilan Gorur, Yee Teh

Abstract

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.