Systems
Required Reading
Books
- 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.
- A. Griewank and A. Walther, Evaluating derivatives: principles and techniques of algorithmic differentiation, vol. 105. Siam, 2008.
Papers
As listed on syllabus.
Recommended Reading
Books
- N. D. Goodman and A. Stuhlmüller, “The Design and Implementation of Probabilistic Programming Languages.” 2014.
- A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian data analysis. Chapman and Hall/CRC, 1995.
- C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics),” 2006.
- R. M. Neal, “Probabilistic inference using Markov chain Monte Carlo methods,” 1993.