Efficient Sparse Bayesian Learning using Spike-and-Slab Priors

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi

Contributors dc:contributor

• Yuan Qi
• Charles A Bouman
• Jennifer Neville
• David F Gleich
• Ninghui Li

• Classification
• Regression
• Sparse Bayesian Learning
• Statistical Machine Learning
• Supervised Learning
• Variable Selection

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1402

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2618