Resources and References
This page lists
various resources that may be of use in understanding the underlying
concepts behind Infer.NET, both at the introductory and more technical
level. Two excellent self-contained starting points are:
Both describe graphical models in general, specific models which we provide
as Infer.NET examples, and the approximate inference algorithms available in
Infer.NET.
Probabilistic models
- Kevin Murphy's book Machine Learning: a Probabilistic Perspective
gives excellent coverage of probabilistic Machine Learning with plenty of
examples - it is targeted at undergraduates as well as graduates.
- Kevin Murphy also has
a good tutorial paper
that covers
probabilistic models and
Bayesian networks:
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
- The chapters on Uncertainty and Probablistic Modelling (13 to 15) in Russel
and Norvig's standard AI text (3rd edition) have been recommended on the forum.
- Daphne Koller teaches an online course on Probabilistic Graphical Models:
https://www.coursera.org/#course/pgm
- Sebastian Thrun and Peter Norvig gave an online course course "Introduction
to Artificial Intelligence" which covers many of the Infer.NET concepts at:
https://www.ai-class.com/
Approximate Inference