قارن الطرق

	شبكة بايزيانية	شبكة الذاكرة قصيرة وطويلة الأمد
المجال	بايزي	التعلم العميق
العائلة	Bayesian methods	Machine learning
سنة النشأة	1988	1997
صاحب الطريقة	Judea Pearl	Hochreiter, S. & Schmidhuber, J.
النوع	Probabilistic graphical model	Recurrent neural network (gated memory cell)
المصدر التأسيسي	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
الأسماء البديلة	Bayes network, belief network, probabilistic graphical model, directed graphical model	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
ذات صلة	4	5
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.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.