Parameter Learning of Bayesian Network with Multiplicative Synergistic Constraints
Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. The traditional approach to this challenge is introducing domain knowledge/expert judgments that ar...
Main Authors: | Yu Zhang, Zhiming Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-07-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/14/7/1469 |
Similar Items
-
Implicit parameter estimation for conditional Gaussian Bayesian networks
by: Aida Jarraya, et al.
Published: (2014-01-01) -
Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach
by: Das, Monidipa, et al.
Published: (2022) -
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
by: Andrea Ruggieri, et al.
Published: (2020-12-01) -
dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential Evolution
by: Alejandro Platas-López, et al.
Published: (2023-07-01) -
Bayesian network parameter learning algorithm based on improved QMAP
by: Di Ruohai, et al.
Published: (2021-12-01)