
Inconsistency of PitmanYor process mixtures for the number of components
In many applications, a finite mixture is a natural model, but it can be...
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On the Identifiability of Finite Mixtures of Finite Product Measures
The problem of identifiability of finite mixtures of finite product meas...
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Mixture modeling on related samples by ψstick breaking and kernel perturbation
There has been great interest recently in applying nonparametric kernel ...
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On the expected number of components in a finite admixture model
In this paper we describe the growth rate of the expected number of comp...
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Pull Message Passing for Nonparametric Belief Propagation
We present a "pull" approach to approximate products of Gaussian mixture...
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Hadamard Powers and the Identification of Mixtures of Products
The Hadamard Power of a matrix is the matrix consisting of all Hadamard ...
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Finite Mixtures of ERGMs for Ensembles of Networks
Ensembles of networks arise in many scientific fields, but currently the...
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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data points into clusters such that the variables are jointly conditionally independent given the cluster. This method can be used to identify finite confounders.
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