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Efficient Sequential Monte Carlo Inference for Kingman's Coalescent

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Algorithms for automatically discovering hierarchical structure from data play an important role in machine learning. Teh et al. (2008) proposed a Bayesian hierarchical clustering model based on Kingman’s coalescent and proposed both greedy and sequential Monte Carlo (SMC) based agglomerative clustering algorithms for inference, the SMC algorithm having computational cost cubic in the number of data points per particle. We build upon this work and propose a new SMC based algorithm for inference in the coalescent clustering model where the computations required to consider merging each pair of clusters at each iteration is not discarded in subsequent iterations. This improves the computational cost to be quadratic per particle. In experiments we show that our new algorithm achieves improved costs without sacrificing accuracy or reliability.

This talk is part of the Machine Learning @ CUED series.

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