Resources for learning applied Bayesian analysis, with a focus on using Python
- Aki Vehtari course in Bayesian Analysis
Stan MCMC
- Stan MCMC Software
Stan MCMC is what you should be using to do your analysis. It is robust, fast, and relatively easy to use. It has interfaces with R, Python, Matlab, and Stata. But come on, get with it, you should be using R or Python.
- Bayesian Decision Theory Made Simple
- Classical IV in Stan
- Michael Betancourte's case studies of, among other things, gaussian processes.
- Pystan workflow
- Modern Statistical Workflow
Jim Savage's blog. Includes a lot of nice posts that use Stan in modelling issues in economics, statistics and machine learning applications.
- Talks by Michael Betancourt
Betancourt is one of the developers of Stan, and he gives good talks explaining what is going on behind the scenes. Spoiler: it involves some 3-d advanced donut geometry
- Applied longitudinal data analysis in brms and the tidyverse
Bayesian analysis with Python
- Frequentism and Bayesianism: A Python-driven Primer or blog version
- Tutorials for Bayesian analysis with PyStan
- Kalman and Bayesian Filtering in Python
- Predicting future returns of trading algorithms: Bayesian Cone.
- Bayesian analysis in Python
- Non-parametric Bayes in Python
- Probabilistic Programming and Bayesian Methods for Hackers
- Bayesian Modelling and Computation in R
Other links
- Not necessarily Bayesian, but a cool website for visualizing probability.