Learning Bayesian networks with biomedical applications

Cassio P. de Campos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


This talk presents a brief overview of methods for learning Bayesian networks. It discusses on recent methods and theoretical results to speed up computations and to improve accuracy, leading to an approach which can deal with many thousands of variables. Applications arising in biomedical problems are described, where it is argued that Bayesian networks can provide meaningful and interpretable results. In particular, we discuss on the use of Bayesian networks for data imputation, unsupervised clustering and classification using high-dimensional data sets of lymphoma patients.

Original languageEnglish
Title of host publicationAdvanced Methodologies for Bayesian Networks - 2nd International Workshop, AMBN 2015, Proceedings
EditorsJoe Suzuki, Maomi Ueno
Number of pages1
ISBN (Print)9783319283784
Publication statusPublished - 2015
Externally publishedYes
Event2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015 - Yokohama, Japan
Duration: 16 Nov 201518 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015

Bibliographical note

Included in the Preface of Springer LNAI 9505


  • Bayesian networks
  • Clustering
  • Data imputation
  • Structure learning


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