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Other software that way be useful for implementing Gaussian process models:

- The NETLAB package by Ian Nabney includes code for Gaussian process regression and many other useful thing, . optimisers.
- See Tom Minka 's page on accelerating matlab and his lightspeed toolbox.
- Matthias Seeger shares his code for Kernel Multiple Logistic Regression, Incomplete Cholesky Factorization and Low-rank Updates of Cholesky Factorizations.
- See the software section of - .

Annotated Bibliography Below is a collection of papers relevant to learning in Gaussian process models. The papers are ordered according to topic, with occational papers occuring under multiple headings. [ Tutorials | Regression | Classification | Covariance Functions | Model Selection | Approximations | Stats | Learning Curves | RKHS | Reinforcement Learning | GP-LVM | Applications | Other Topics ]

Tutorials Several papers provide tutorial material suitable for a first introduction to learning in Gaussian process models. These range from very short [ Williams 2002 ] over intermediate [ MacKay 1998 ], [ Williams 1999 ] to the more elaborate [ Rasmussen and Williams 2006 ]. All of these require only a minimum of prerequisites in the form of elementary probability theory and linear algebra. D. J. C. MacKay. Information Theory, Inference and Learning Algorithms . Cambridge University Press, Cambridge, UK, 2003. chapter 45 . Comment: A short introduction to GPs, emphasizing the relationships to paramteric models (RBF networks, neural networks, splines).

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