2008
NeurIPS
NeurIPS 2008
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Trend Setter
- Sampling
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Hot Topic Early Bird
- sequential monte carlo
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Machine Learning > Optimization & Theory > Bayesian Inference
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Stochastic Methods
Machine Learning > Learning Types > Sampling