Jami J. Mulgrave

MathematicsChemistryComputer Science

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 4 papers. Top paper: “Regression‐based Bayesian estimation and structure learning for nonparanormal graphical models”.

Regression‐based Bayesian estimation and structure learning for nonparanormal graphical models

Statistical Analysis and Data Mining The ASA Data Science Journal(2022)10.1002/sam.11576

N/A

0.208DataRank · unranked

3 citations

Subhashis Ghosal, Jami J. Mulgrave

Bayesian Posterior Interval Calibration to Improve the Interpretability of Observational Studies

arXiv (Cornell University)(2020)10.48550/arxiv.2003.06002

N/A

0DataRank · unranked

0 citations

David Madigan, George Hripcsak, Jami J. Mulgrave

Bayesian analysis of nonparanormal graphical models using rank-likelihood

Journal of Statistical Planning and Inference(2022)10.1016/j.jspi.2022.06.006

N/A

0DataRank · unranked

0 citations

Subhashis Ghosal, Jami J. Mulgrave

Bayesian Posterior Interval Calibration to Improve the Interpretability of Observational Studies

Statistical Analysis and Data Mining The ASA Data Science Journal(2024)10.1002/sam.11715

N/A

0DataRank · unranked

0 citations

David Madigan, George Hripcsak, Jami J. Mulgrave