.. _Resources:

Software
--------

- `Stan <https://mc-stan.org>`_ is our main engine for Bayesian inference. You will refer to its `documentation <https://mc-stan.org/users/documentation/>`_ many times throughout the term.
- `CmdStanPy <https://cmdstanpy.readthedocs.io/>`_ if our primary interface to Stan.
- `ArviZ <https://arviz-devs.github.io/arviz/>`_ provides valuable tools for parsing Stan results.
- `The Distribution Explorer <https://distribution-explorer.github.io>`_ provides quick references for various distributions and the syntax for using them in Python and Stan.
- `The bebi103 package <https://bebi103.github.io>`_ provides utility functions for MCMC-based inference and other useful functionality for the class.


Reading/tutorials
-----------------

- `Michael Betancourt's writings <https://betanalpha.github.io/writing/>`_ contain some of the best explanations and case studies about Bayesian inference, especially with Stan, you can find anywhere.
- `Bayesian Data Analysis, 3rd Ed. <http://www.stat.columbia.edu/~gelman/book/>`_ by Gelman, et al., is for many researchers the primary reference for Bayesian inference. The authors have made it freely available at `this link <http://www.stat.columbia.edu/~gelman/book/BDA3.pdf>`_.
- `Stan tutorials YouTube channel <https://www.youtube.com/playlist?list=PLCrWEzJgSUqwL85xIj1wubGdY15C5Gf7H>`_ has a great series of videos.
- `Statistical Rethinking, 2nd Ed. <https://xcelab.net/rm/statistical-rethinking/>`_ by Richard McElreath is a good pedagogical book about Bayesian inference.
- `A Student's Guide to Bayesian Statistics <https://ben-lambert.com/a-students-guide-to-bayesian-statistics/>`_ by Ben Lambert is another good reference, especially for beginners.