Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clustering in recovering the latent labels of i.i.d. samples from a finite mixture...

Gaussian mixture model simplification is a powerful technique for reducing the number of components of an existing mixture model without having to re-cluster the original data set. Instead, a simplified GMM with fewer components is computed by minimizing some distance metric between the two models. In this paper, we derive an analytical expression for the difference between the probability dens...

Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clustering in recovering the latent labels of i.i.d. samples from a finite mixture...

A sequential Bayesian phase II/III design is proposed for comparative clinical trials. The design is based on both survival time and discrete early events that may be related to survival and assumes a parametric mixture model. Phase I1 involves a small number of centers. Patients are randomized between treatments throughout, and sequential decisions are based on predictive probabilities of conc...

Many diffusion-ordered spectroscopy (DOSY) NMR techniques have recently been developed to aid in the deconvolution of complex mixtures. Spectroscopic separation based on chemical and physical properties facilitates the identification of mixture components while eliminating time-consuming separation steps and preserving the chemical environment. One way to improve resolution in such experiments ...