C
Cassio P. de Campos
Researcher at Utrecht University
Publications - 116
Citations - 2167
Cassio P. de Campos is an academic researcher from Utrecht University. The author has contributed to research in topics: Bayesian network & Graphical model. The author has an hindex of 23, co-authored 116 publications receiving 1969 citations. Previous affiliations of Cassio P. de Campos include Association for Computing Machinery & University of São Paulo.
Papers
More filters
Journal ArticleDOI
Efficient Structure Learning of Bayesian Networks using Constraints
Cassio P. de Campos,Qiang Ji +1 more
TL;DR: A branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality and the benefits of using the properties with state-of-the-art methods and with the new algorithm, able to handle larger data sets than before.
Journal ArticleDOI
Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome
Andrea Rinaldi,Michael Mian,Ekaterina Chigrinova,Luca Arcaini,Govind Bhagat,Urban Novak,Paola M.V. Rancoita,Cassio P. de Campos,Francesco Forconi,Randy D. Gascoyne,Fabio Facchetti,Maurilio Ponzoni,Silvia Govi,Andrés J.M. Ferreri,Manuela Mollejo,Miguel A. Piris,Luca Baldini,Jean Soulier,Catherine Thieblemont,Vincenzo Canzonieri,Valter Gattei,Roberto Marasca,Silvia Franceschetti,Gianluca Gaidano,Alessandra Tucci,Silvia Uccella,Maria Grazia Tibiletti,Stephan Dirnhofer,Claudio Tripodo,Claudio Doglioni,Riccardo Dalla Favera,Franco Cavalli,Emanuele Zucca,Ivo Kwee,Francesco Bertoni +34 more
TL;DR: Although del(17p) did not determine a worse outcome and del(8p) was only of borderline significance, the presence of both deletions had a highly significant negative impact on the outcome of splenic MZLs.
Proceedings ArticleDOI
Structure learning of Bayesian networks using constraints
TL;DR: This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable by presenting a branch and bound algorithm that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function.
Proceedings Article
Learning Bayesian networks with thousands of variables
TL;DR: A novel algorithm that effectively explores the space of possible parent sets of a node on the basis of an approximated score function that is computed in constant time and an improvement of an existing ordering-based algorithm for structure optimization.
Proceedings Article
The inferential complexity of Bayesian and credal networks
TL;DR: A new class of networks with bounded width is defined, and a new decision problem is introduced for Bayesian networks, the maximin a posteriori.