Required Reading


  1. J. W. van de Meent, B. Paige, H. Yang, and F. Wood, “Introduction to Probabilistic Programming,” Foundations and Trends in Machine Learning, pp. in review, 2018.
  2. A. Griewank and A. Walther, Evaluating derivatives: principles and techniques of algorithmic differentiation, vol. 105. Siam, 2008.


As listed on syllabus.


  1. N. D. Goodman and A. Stuhlmüller, “The Design and Implementation of Probabilistic Programming Languages.” 2014.
  2. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian data analysis. Chapman and Hall/CRC, 1995.
  3. C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics),” 2006.
  4. R. M. Neal, “Probabilistic inference using Markov chain Monte Carlo methods,” 1993.