Graphical Model Learning Algorithm

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.

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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

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