Description
As a foundational tutorial in statistics and Bayesian inference, the intended audience is Pythonistas who are interested in gaining a foundational knowledge of probability theory and the basics of parameter estimation.
After attending this tutorial, participants will have a solid foundation of probability viewed through the lens of computational simulation and see how probability distributions can be matched to real-world data generating processes. Participants will also understand how to use numpy.random to simulate draws from a probability distribution, use those simulations to calculate summary statistics, and use those summary statistics in testing hypotheses against data in a Bayesian fashion. Participants will also be given an introductory taste of a probabilistic programming language.
Knowledge of numpy, matplotlib, and Python are prerequisites for this tutorial, in addition to curiosity and an excitement to learn new things!
https://github.com/ericmjl/bayesian-stats-modelling-tutorial