Carl Smith and Ari Pakman: Feb. 20

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

Outline:

- 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