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June 2019 Analysis of the Maximal a Posteriori Partition in the Gaussian Dirichlet Process Mixture Model
Łukasz Rajkowski
Bayesian Anal. 14(2): 477-494 (June 2019). DOI: 10.1214/18-BA1114


Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components. To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs). It is therefore important to develop an understanding of the strengths and weaknesses of this approach. This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution). Some desirable properties of the MAP partition are proved: ‘almost disjointness’ of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity). Consequently, the number of such clusters remains bounded. Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter. As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.


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Łukasz Rajkowski. "Analysis of the Maximal a Posteriori Partition in the Gaussian Dirichlet Process Mixture Model." Bayesian Anal. 14 (2) 477 - 494, June 2019.


Published: June 2019
First available in Project Euclid: 30 July 2018

zbMATH: 07045439
MathSciNet: MR3934094
Digital Object Identifier: 10.1214/18-BA1114

Primary: 62F15


Vol.14 • No. 2 • June 2019
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