Probabilistic programming

Content

• Hamiltonian Monte Carlo
• Probabilistic programming
• Stan

Learning objectives

At the end of the chapter, students should be able to

• code models using Stan

Readings

• Betancourt (2017)
• Neal (2011)
• Chi Feng’s MCMC gallery

Complementary readings

• Stan user guide

Slides

 View all slides in new window  Download PDF of all slides

Code

• Stochastic volatility model: Stan code 1 and 2 and cmdstanr R script
• Smartwatch example and cmdstanr R script

References

Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv Preprint. https://doi.org/10.48550/arXiv.1701.02434

Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X. L. Meng (Eds.), Handbook of Markov chain Monte Carlo (pp. 113–162). CRC Press. https://doi.org/10.1201/b10905-5