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Computing conditional probabilities in Bayesian networks using logistic regression

delete2026-04-19
delete0
PRE
AI
S
Serafı́n Moral
S
Serafín Moral‐García *
A
Andrés Cano
M
Manuel Gómez‐Olmedo
DOI：10.1016/j.asoc.2026.115284delete
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Abstract

Abstract

En 中文
• In Bayesian networks, parameters grow exponentially with the number of parents. • Enhanced logistic regression uses synthetic variables to improve model flexibility. • New variables are created via XOR combinations and LDA-based discretization. • The paper compares transformations via completeness, equivalence, and subsumption. • LDA-based logistic regression excels with fewer parameters than traditional tables.
Keywords:
Bayesian networks
logistic regression
parameter reduction
synthetic variables
LDA-based discretization

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

No organization information available