Neural Network Graphical model
The following excerpt from “A Brief Introduction to Graphical Models and Bayesian Networks” by Kevin Murphy.
In reverse chronological order.
- Daphne Koller and Nir Friedman, “Probabilistic graphical models: principles and techniques”, MIT Press 2009
- Adnan Darwiche, “Modeling and reasoning with Bayesian networks”, Cambridge 2009
- F. V. Jensen. “Bayesian Networks and Decision Graphs”. Springer. 2001.
Probably the best introductory book available.
- D. Edwards. “Introduction to Graphical Modelling”, 2nd ed. Springer-Verlag. 2000.
Good treatment of undirected graphical models from a statistical perspective.
- J. Pearl. “Causality”. Cambridge. 2000.
The definitive book on using causal DAG modeling.
- R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. “Probabilistic Networks and Expert Systems”. Springer-Verlag. 1999.
Probably the best book available, although the treatment is restricted to exact inference.
- M. I. Jordan (ed). “Learning in Graphical Models”. MIT Press. 1998.
Loose collection of papers on machine learning, many related to graphical models. One of the few books to discuss approximate inference.
- B. Frey.
Source: 52nlp's Learning Notes
Graphical Models: Foundations of Neural Computation (Computational Neuroscience)
Book (A Bradford Book)
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