قارن الطرق

	شبكة بايزيانية	الانحدار اللوجستي
المجال	بايزي	إحصاء البحث
العائلة	Bayesian methods	Process / pipeline
سنة النشأة	1988	1958
صاحب الطريقة	Judea Pearl	David Roxbee Cox
النوع	Probabilistic graphical model	Method
المصدر التأسيسي	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
الأسماء البديلة	Bayes network, belief network, probabilistic graphical model, directed graphical model	logit model, binomial logistic regression, LR
ذات صلة	4	3
الملخص	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.