Nourishing reading

Here is a list of resources that contain great expositions of different aspects of Bayesian statistics and machine learning, in a complementary discursive manner. It is meant as a list of deeper dives you can take, perhaps after completing the course if you wish. The hefty list of titles on the homepage also contain a lot of good discussions, insights, and examples that are not in the lecture notes.

• Elreath, R., Statistical rethinking: a Bayesian course with examples in R and STAN, 2nd ed., Chapman & Hall, 2020.
  

• Jaynes, E. T. "Prior probabilities." IEEE Transactions on systems science and cybernetics 4.3 (1968): 227-241. https://bayes.wustl.edu/etj/articles/prior.pdf Links to an external site.
  
• Gelman, Andrew, et al. "Bayesian workflow." arXiv preprint arXiv:2011.01808 (2020). https://arxiv.org/abs/2011.01808 Links to an external site.
  
• Ghahramani, Iain, and Murray Zoubin. "A note on the evidence and Bayesian Occam’s razor." Gatsby Unit Technical Report, 2005. https://homepages.inf.ed.ac.uk/imurray2/pub/05occam/occam.pdf Links to an external site.
  
• Kass, Robert E., and Adrian E. Raftery. "Bayes factors." Journal of the American statistical association 90.430 (1995): 773-795. https://www.andrew.cmu.edu/user/kk3n/simplicity/KassRaftery1995.pdf Links to an external site.
  
• Neal, Radford M. "MCMC using Hamiltonian dynamics." arXiv preprint arXiv:1206.1901 (2012). https://arxiv.org/abs/1206.1901 Links to an external site.
• Examples of GP regression:
  • Jan Hendrik Metzen, Guillaume Lemaitre. "Illustration of prior and posterior Gaussian process for different kernels" https://scikit-learn.org/1.5/auto_examples/gaussian_process/plot_gpr_prior_posterior.html Links to an external site.
  • Jan Hendrik Metzen, Guillaume Lemaitre. "Ability of Gaussian process regression (GPR) to estimate data noise-level" https://scikit-learn.org/1.5/auto_examples/gaussian_process/plot_gpr_noisy.html Links to an external site.
  • Jan Hendrik Metzen, Guillaume Lemaitre. "Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)" https://scikit-learn.org/1.5/auto_examples/gaussian_process/plot_gpr_co2.html Links to an external site.
• Deisenroth, Marc, and Carl E. Rasmussen. "PILCO: A model-based and data-efficient approach to policy search." Proceedings of the 28th International Conference on machine learning (ICML-11). 2011. https://mlg.eng.cam.ac.uk/pub/pdf/DeiRas11.pdf Links to an external site.
  
• Achiam, Josh. "Spinning Up in Deep RL" https://spinningup.openai.com/en/latest/ Links to an external site.
• Jacot, Arthur, Franck Gabriel, and Clément Hongler. "Neural tangent kernel: Convergence and generalization in neural networks." Advances in neural information processing systems 31 (2018). https://arxiv.org/abs/1806.07572 Links to an external site.
  
• Rahimi, Ali, and Benjamin Recht. "Random features for large-scale kernel machines." Advances in neural information processing systems 20 (2007). https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf Links to an external site.
  

Let me know if you have found something illuminating, no matter what technical level, that you think should be on this list, and I will add it.