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.

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

No comments:

Post a Comment

Note: Only a member of this blog may post a comment.