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Approximation and Inference using Latent Variable Sparse Linear Models

Approximation and Inference using Latent Variable Sparse Linear Models

This video was recorded at NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, Whistler 2007. A variety of Bayesian methods have recently been introduced for performing approximate inference using linear models with sparse priors. We focus on four methods that capitalize on latent structure inherent in sparse distributions to perform: (i) standard MAP estimation, (ii) hyperparameter MAP estimation (evidence maximization), (iii) variational Bayes using a factorial posterior, and (iv) local variational approximation using convex lower bounding. All of these approaches can be used to compute Gaussian posterior approximations to the underlying full distribution; however, the exact nature of these approximations is frequently unclear and so it is a challenging task to... Show More

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