Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian...
Main Authors: | Pablo de Morais Andrade, Julio Michael Stern, Carlos Alberto de Bragança Pereira |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2014-03-01
|
Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/16/3/1376 |
Similar Items
-
Approximate Learning of High Dimensional Bayesian Network Structures via Pruning of Candidate Parent Sets
by: Zhigao Guo, et al.
Published: (2020-10-01) -
Improved Local Search with Momentum for Bayesian Networks Structure Learning
by: Xiaohan Liu, et al.
Published: (2021-06-01) -
Computation of Kullback–Leibler Divergence in Bayesian Networks
by: Serafín Moral, et al.
Published: (2021-08-01) -
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism
by: Nataliya Sokolovska, et al.
Published: (2021-07-01) -
Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey
by: Pedro Shiguihara, et al.
Published: (2021-01-01)