An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering

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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Dilan Görür, Yee Whye Teh
• Keywords: Carlo Inference Scheme, Efficient Sequential Monte, Information Technology, NIPS 2008, Sequential Monte Carlo