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NIPS
2008

An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering

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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.
Dilan Görür, Yee Whye Teh
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Dilan Görür, Yee Whye Teh
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