{"id":{"repo_id":"purdue-thes","oai_identifier":"oai:docs.lib.purdue.edu:open_access_dissertations-2618"},"canonical_url":"https://search.dev.ndltd.org/etd/purdue-thes/oai:docs.lib.purdue.edu:open_access_dissertations-2618","repository":{"repo_id":"purdue-thes","name":"Purdue University","base_url":"https://docs.lib.purdue.edu/do/oai/"},"display":{"title":"Efficient Sparse Bayesian Learning using Spike-and-Slab Priors","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Yuan Qi","Charles A Bouman","Jennifer Neville","David F Gleich","Ninghui Li"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T03:54:38Z","subjects":["Classification","Regression","Sparse Bayesian Learning","Statistical Machine Learning","Supervised Learning","Variable Selection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://docs.lib.purdue.edu/open_access_dissertations/1402","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yuan Qi","Charles A Bouman","Jennifer Neville","David F Gleich","Ninghui Li"]},{"key":"dc:creator","label":"Author","values":["Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Classification","Regression","Sparse Bayesian Learning","Statistical Machine Learning","Supervised Learning","Variable Selection"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://docs.lib.purdue.edu/open_access_dissertations/1402"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Efficient Sparse Bayesian Learning using Spike-and-Slab Priors"]}]}],"canonical_facts":{"dc:contributor":["Yuan Qi","Charles A Bouman","Jennifer Neville","David F Gleich","Ninghui Li"],"dc:creator":["Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi"],"dc:description.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."],"dc:identifier":["https://docs.lib.purdue.edu/open_access_dissertations/1402"],"dc:subject":["Classification","Regression","Sparse Bayesian Learning","Statistical Machine Learning","Supervised Learning","Variable Selection"],"dc:title":["Efficient Sparse Bayesian Learning using Spike-and-Slab Priors"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:54:38Z"}