Veroniká Rockova (Wharton Business School): Sequential EMVS Mode Detection for Posterior Reconstruction

EMVS is a fast deterministic approach to identifying sparse high posterior models for Bayesian variable selection under spike-and-slab priors. In large high-dimensional problems where exact full posterior inference must be sacrificed for computational feasibility, sequentially reinitialized deployments of EMVS can be used to find subsets of the highest posterior modes. These EMVS identified modes can then be used as a basis for a truncated reconstruction of the full parameter space posterior. Obtained as a posterior model probability weighted sum of expressions for model coefficient posterior distributions, this reconstruction is a rapidly computable closed form expression that yields fast approximations for model averaging and the median probability model.

Joint work with Edward George

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