An empirical Bayes approach for learning directed acyclic graph using MCMC algorithm-Reference-Cited by-同舟云学术

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An empirical Bayes approach for learning directed acyclic graph using MCMC algorithm

Published:2019-07-17 Issue:5 Volume:12 Page:394-403
ISSN:1932-1864
Container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal
language:en
Short-container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal

Author:

Rezaei Tabar Vahid¹^ORCID,Zareifard Hamid²,Salimi Selva³,Plewczynski Dariusz⁴⁵

Affiliation:

1. Department of Statistics, Faculty of Mathematical Sciences and ComputerAllameh Tabataba'i University Tehran Iran

2. Department of StatisticsJahrom University Jahrom Iran

3. Department of Information Technology ManagementKharazmi University Tehran Iran

4. Laboratory of Functional and Structural Genomics, Center of New TechnologiesUniversity of Warsaw Warsaw Poland

5. Faculty of Mathematics and Information ScienceWarsaw University of Technology Warsaw Poland

Funder

Fundacja na rzecz Nauki Polskiej

Narodowe Centrum Nauki

Publisher

Wiley

Subject

Computer Science Applications,Information Systems,Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11430

Reference33 articles.

1. Hailfinder: A Bayesian system for forecasting severe weather

2. Prentice's Approach and the Meta-Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints

3. I. A.Beinlich H. J.Suermondt R. M.Chavez andG. F.Cooper The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. AIME 89 1989 Springer pp. 247–256.

4. B.Boerlage Link strength in Bayesian networks. Ph.D. Thesis University of British Columbia.

5. On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

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