An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering
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.
Cite
Text
Gorur and Teh. "An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering." Neural Information Processing Systems, 2008.Markdown
[Gorur and Teh. "An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/gorur2008neurips-efficient/)BibTeX
@inproceedings{gorur2008neurips-efficient,
title = {{An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering}},
author = {Gorur, Dilan and Teh, Yee W.},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {521-528},
url = {https://mlanthology.org/neurips/2008/gorur2008neurips-efficient/}
}