1. Version 1.

• Tutorial introduction to Gaussian process
  • Gaussian Processes for Regression: A Quick Introduction
  • Tutorial: Gaussian process models for machine learning
  • Application to differential equations
• Population-based sampling method
  • http://people.cs.ubc.ca/~murphyk/MLRG/MLRGpopulationMC.pdf
  • On population-based simulation for static inference
• On the estimation of regression for Gaussian process
  • An R package
• Applications in psychology
  • On human learning
  • SEM models Matlab codes
  • A Gaussian process prior
• Software
  • Some Bayesian software
  • Bayesian functional data analysis using WinBUGS
  • Pymc - MCMC for Python
  • Codes for the book Gaussian process for machine learning

[7] Mayraz, G. and Hinton, G. (2001) Recognizing Hand-Written Digits Using Hierarchical Products of Experts, Proc. NIPS 13.

[13] Rasmussen, C.E. and Williams, C.K.I. (2006) Gaussian Processes for Machine Learning, The MIT Press.

[15] Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J. and Rasmussen, C.E. (2003) Derivative observations in Gaussian Process models of dynamic systems, Proc. NIPS 15.

[20] |Williams, C.K.I., Agakov, F.V., Felderof, S.N. (2002), Products of Gaussians, Proc. NIPS 14.

• Girolami 2008
• Sarkka 2006
• Calderhead et al. 2008
• http://videolectures.net/dsb06_whistler/
• http://hal.archives-ouvertes.fr/docs/00/36/01/11/PDF/donnet_foulley_samson.pdf

• http://www.cs.unc.edu/~welch/media/pdf/maybeck_ch1.pdf
• http://www.cs.unc.edu/~tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf
• http://www.cs.unc.edu/~welch/kalman/

• http://www.gaussianprocess.org/
• http://gaussianprocess.com/
• http://publications.nr.no/917_Rapport.pdf

Using WinBUGS for data analysis