Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
and Harrison, Matthew T
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces MFM-Wishart for clustering covariance matrices, with posterior consistency theory, MCMC algorithm, simulations, and application to infant brain functional connectivity.
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Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
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Mixture-of-Finite-Mixtures Wishart Model for Clustering Covariance Matrices with an Application to Brain Functional Connectivity
Introduces MFM-Wishart for clustering covariance matrices, with posterior consistency theory, MCMC algorithm, simulations, and application to infant brain functional connectivity.