Date of Award
January 2016
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Yuan Qi
Committee Member 1
Charles A Bouman
Committee Member 2
Jennifer Neville
Committee Member 3
David F Gleich
Committee Member 4
Ninghui Li
Abstract
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly encode this tradeoff in a principled manner.
Recommended Citation
Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi, "Efficient Sparse Bayesian Learning using Spike-and-Slab Priors" (2016). Open Access Dissertations. 1402.
https://docs.lib.purdue.edu/open_access_dissertations/1402
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