Graphical Model Learning Algorithm
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A Graphical Model Learning Algorithm is a statistical learning algorithm that can solve a Graphical Model Learning Task (that requires a probabilistic graphical model).
- AKA: Probabilistic Graphical Model Learning Algorithm.
- Context:
- It can be a Bayesian Network Training Algorithm.
- See: Nonparametric Bayesian Learning Algorithm.
References
2006
- (Bishop, 2006) ⇒ Christopher M. Bishop. (2006). “Pattern Recognition and Machine Learning. Springer, Information Science and Statistics.
2001
- (Jensen, 2001) ⇒ F. V. Jensen. (2001). “Bayesian Networks and Decision Graphs." Springer.
- Introductory book.
1998
- (Jordan, 1998) ⇒ Michael I. Jordan (ed). (1998). “Learning in Graphical Models. MIT Press.
- Collection of papers. Discusses approximate inference.
- (Murphy, 1998) ⇒ Kevin Murphy. (1998). “A Brief Introduction to Graphical Models and Bayesian Networks."
1997
- (Jordan, 1997) ⇒ Michael I. Jordan. (1997). “An Introduction to Graphical Models." Tutorial at NIPS-1997.
- (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill.
1996
- (Lauritzen, 1996) ⇒ S. Lauritzen. (1996). “Graphical Models.” Oxford.
- mathematical exposition of the theory of graphical models.
1988
- (Pearl, 1998) ⇒ Judea Pearl. (1988). “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
- The seminal book on the graphical models