{"doi":"10.5705/ss.202021.0245","title":"A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models","abstract":"The problem of joint estimation of multiple graphical models from high dimensional data has been studied in the statistics and machine learning literature, due to its importance in diverse fields including molecular biology, neuroscience and the social sciences.This work develops a Bayesian approach that decomposes the model parameters across the multiple graphical models into shared components across subsets of models and edges, and idiosyncratic ones.Further, it leverages a novel multivariate prior distribution, coupled with a jointly convex regression based pseudo-likelihood that enables fast computations through a robust and efficient Gibbs sampling scheme.We establish strong posterior consistency for model selection under high dimensional scaling, with the number of variables growing exponentially as a function of the sample size.The efficiency of the proposed approach in borrowing strength across models to identify jointly shared edges is illustrated on both synthetic and real data.","journal":"Statistica Sinica","year":2022,"id":288080,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.956,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":960374,"name":"Kshitij Khare","orcid":"0000-0001-8595-7503","position":1,"is_corresponding":false},{"id":514917,"name":"George Michailidis","orcid":"0000-0002-3676-1739","position":2,"is_corresponding":false},{"id":968514,"name":"Peyman Jalali","orcid":"0000-0002-7010-1742","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-19T00:30:11.069895Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}