avehtari
commented
4 years ago Bayesian Data Analysis. Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. CRC, 2013.
NOTE: soon a new edition, codebook by @avehtari here, book release page: https://www.cambridge.org/core/books/regression-and-other-> stories/DD20DD6C9057118581076E54E40C372C (likely comes sooner than in September)
- Link for the BDA3 PDF for non-commercial purposes https://users.aalto.fi/~ave/BDA3.pdf
- Course material for BDA3 including video lectures, slides, and notes for most of the chapters https://github.com/avehtari/BDA_course_Aalto
- Regression and other stories is not a new edition of BDA3, but revised version of half of "Data Analysis Using Regression and Multilevel/Hierarchical Models"
epogrebnyak
HT @rahuldave
Great outline, see also course textbooks in specific in syllabus:
The following book is required for the course:
Machine Learning: A Probabilistic PerspectiveKevin P. Murphy, MIT Press, 2012. This is a verynew book that covers a wide set of important topics. As the book is fresh and comprehensive,there are still quite a few errors. We will try to maintain lists of errata as they are discovered. The following book is strongly recommended, but not required:
Pattern Recognition and Machine LearningChristopher M. Bishop, Springer, 2006. An excellentand affordable book on machine learning, with a Bayesian focus. It covers fewer topics thanthe Murphy book, but goes into greater depth on many of them and you may find that youprefer Bishop’s exposition.
These are other (free online!) books on machine learning and related topics that you may findhelpful, but that are completely optional.:
Information Theory, Inference, and Learning AlgorithmsDavid J.C. MacKay, Cambridge Uni-versity Press, 2003. Freely available online athttp://www.inference.phy.cam.ac.uk/mackay/itila/. A very well-written book with excellent explanations of many ma-chine learning topics.
Bayesian Reasoning and Machine LearningDavid Barber, Cambridge University Press, 2012. Freelyavailable online athttp://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online.3
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer,2009. Freely available online athttp://www-stat.stanford.edu/ ̃tibs/ElemStatLearn/.
These are books on some specialized topics that you may find useful:
Gaussian Processes for Machine Learning. Carl Edward Rasmussen and Christopher K.I. Williams,MIT Press, 2006. Freely available online athttp://www.gaussianprocess.org/gpml/.
Non-Uniform Random Variate GenerationLuc Devroye, Springer-Verlag, 1986. Freely availableonline athttp://luc.devroye.org/rnbookindex.html.
Probabilistic Graphical Models: Principles and TechniquesDaphne Koller and Nir Friedman,MIT Press, 2009.
Numerical Optimization Jorge Nocedal and Stephen J. Wright, Springer, 2006.
Bayesian Data Analysis. Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. CRC, 2013.
NOTE: soon a new edition, codebook by @avehtari here, book release page: https://www.cambridge.org/core/books/regression-and-other-stories/DD20DD6C9057118581076E54E40C372C (likely comes sooner than in September)
Elements of Information Theory Thomas M. Cover and Joy A. Thomas, Wiley, 1991.
Monte Carlo Statistical Methods Christian P. Robert and George Casella, Springer, 2005.