Importance Sampling Schemes for Evidence Approximation in Mixture Models

COUV_CAHIER_EGND_A5by Jeong Eun Lee & Christian P. Robert

We produce approximation bounds on a semidefinite programming relaxation for sparse principal component analysis. These bounds control approximation ratios for tractable statistics in hypothesis testing problems where data points are sampled from Gaussian models with a single sparse leading component.

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