Montreal, Canada Thursday 10 April, 1:20 p.m.–4:40 p.m. If you want to follow this tutorial, please read the instructions here to get set up ahead of time. Here are my slides: ## And here is the video:## Bayesian statistics made simpleAllen Downey Audience level: Intermediate ## DescriptionAn introduction to Bayesian statistics using Python. Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally. People who know Python can get started quickly and use Bayesian analysis to solve real problems. This tutorial is based on material and case studies from Think Bayes (O’Reilly Media). ## AbstractBayesian statistical methods are becoming more common and more important, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start. I will present simple programs that demonstrate the concepts of Bayesian statistics, and apply them to a range of example problems. Participants will work hands-on with example code and practice on example problems. Students should have at least basic level Python and basic statistics. If you learned about Bayes’s theorem and probability distributions at some time, that’s enough, even if you don’t remember it! Students should bring a laptop with Python 2.x and matplotlib. You can work in any environment; you just need to be able to download a Python program and run it. Outline: 1. Bayes’s theorem. 2. Representing probability distributions. 3. Bayesian estimation. 4. Biased coins and student test scores. 5. Censored data. 6. The locomotive / German tank problem. 7. Hierarchical models and the hidden species problem. |

Home >