Online Tutorials & Textbooks
- Nonparametric Bayesian Methods. Z. Ghahramani, UAI 2005.
- Dirichlet Processes, Chinese Restaurant Processes, and All That. M. Jordan, NIPS 2005.
- An Introduction to Bayesian Nonparametric Modelling. Y. W. Teh, 2009.
- Structured Bayesian Nonparametric Models and Variational Inference. P. Liang & D. Klein, ACL Tutorial, 2007.
- Foundations of Nonparametric Bayesian Methods. P. Orbanz, 2009.
- A Short Course on Bayesian Nonparametrics. A. Rodriguez, 2011.
- Brown CSCI2950-P: Applied Bayesian Nonparametrics. E. Sudderth, 2011.
- Princeton COS597C: Bayesian Nonparametrics. D. Blei, 2007.
- Gaussian Processes for Machine Learning. C. Rasmussen & C. Williams, MIT Press, 2006.
- Information Theory, Inference, and Learning Algorithms. D. MacKay, Cambridge University Press, 2003.
- The Elements of Statistical Learning. T. Hastie, R. Tibshirani, & J. Friedman, Springer, 2009.
- A Measure Theory Tutorial. M. Gupta, UWEE Technical Report, 2006.
- Convex Optimization. S. Boyd & L. Vandenberghe, Cambridge University Press, 2004.
Reference Print Textbooks
- Bayesian Nonparametrics. N. Hjort, C. Holmes, P. Muller, & S. Walker, Cambridge University Press, 2010.
- Bayesian Nonparametrics. J. Ghosh & R. Ramamoorthi, Springer, 2003.
- Pattern Recognition and Machine Learning. C. Bishop, Springer, 2007.
- Bayesian Data Analysis. A. Gelman, J. Carlin, H. Stern, & D. Rubin, CRC Press, 2003.
- Monte Carlo Statistical Methods. C. Robert & G. Casella, Springer, 2004.
- Sequential Monte Carlo Methods in Practice. A. Doucet, N. de Freitas, & N. Gordon, editors, Springer, 2001.
Software
- Bayesian Modeling and Monte Carlo Methods. R. Neal, University of Toronto.
- Bayesian Nonparametric Mixture Models. Y. W. Teh, Gatsby Unit, University College London.
- Variational Dirichlet Process Mixture Models. K. Kurihara, Google.
- Bayesian Nonparametric Topic Models. D. Blei et al., Princeton University.
- Bayesian Nonparametric Time Series Models. E. Fox, University of Pennsylvania.
- Sequence Memoizer. J. Gasthaus, N. Bartlett, et al.
- Adaptor Grammars. M. Johnson, Macquarie University.
- Gaussian Processes for Machine Learning. C. Rasmussen, University of Cambridge.
- Gaussian Process Latent Variable Models and Sparse Inference. N. Lawrence, University of Sheffield.
- R Package for BNP Analysis. A. Jara, Pontificia Universidad Catolica de Chile.
- Matlab Probabilistic Modeling Toolboxes. K. Murphy, University of British Columbia.
- Lightspeed Matlab Toolbox. T. Minka, Microsoft Research.