Wednesday, February 20, 2013

Carl Smith and Ari Pakman: Feb. 20

We review and present new results on spike-and-slab priors to impose sparsity in regression problems.


- Why spike-and-slab?
- Variational Bayes approximation to the posterior
- Computing hyperparameters using Empirical Bayes.
- Singular and non-singular Markov Chains for MCMC.
- Gibbs sampler for the posterior sparsity variables.
- Extension to regression with positive coefficients.
- Example application: finding synaptic weights in a dendritic tree

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