The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

This article is a Python coding record of Part 3, Chapter 6: 'Dummy Variables and ANOVA Models' from the book 'Introduction to Data Analysis with Bayesian Statistical Modeling using R and Stan'. Part ...