Learning Bayesian networks: approaches and issues

By Rónán Daly, Qiang Shen, Stuart Aitken. 2011

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peter Spirtes
2. Jack Kuipers
3. Giusi Moffa
4. J. Ramsey
5. Gregory F. Cooper
6. Waldemar Karwowski
7. Farzad Vasheghani Farahani
8. Nichole R. Lighthall

In Management Science and Operations Research

1. Peter Spirtes
2. J. Ramsey
3. Gregory F. Cooper
4. Giusi Moffa
5. Jack Kuipers

Citations in the corpus

• Partition MCMC for Inference on Acyclic Digraphs (2015, methodology): “R code is available at https://github.com/annlia/partitionMCMC A rather comprehensive overview of approaches for Bayesian network learning is given by Daly et al. (2011).”
• Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Each node has a probability distribution: In root nodes, this is prior probability, while in child nodes this is the conditional probability (Das, 2004; Daly et al., 2011).”
• Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints (2017, methodology): “General reviews of the topic are in [11,14,22].”