Bayesian Networks

By Marco Scutari, J. Denis. 2014

349 citations recorded by the source corpus.

Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.

Subfields: Artificial Intelligence

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. Stefan Bauer
2. Niklas Pfister
3. Bernhard Schölkopf
4. Jan Peters
5. Sergey Levine
6. J. Urgen Schmidhuber
7. Pieter Abbeel
8. Yoshua Bengio

Citations in the corpus

• Learning stable and predictive structures in kinetic systems (2018, methodology): “Both dynamic Bayesian network methods are implemented in the R-package bnlearn [Scutari and Denis, 2014], where DBN-CondInd uses a conditional independence based algorithm and DBNGreedy a greedy score based algorithm.”