Book Content
chapters • 9h24m total length
1. Thinking probabilistically, a Bayesian inference primer
2. Probabilistic programming, a PyMC3 primer
3. Learning to juggle with more than one parameter - Multiparametric and Hierarchical models
4. Understanding and predicting data with linear regression models
5. Classifying outcomes with logistic regression
6. Too many or too few parameters? Finding the right number the Bayesian way
7. Clustering and learning about subgroups in you data with mixture models
8. Infinitely large models, decision analysis and optimization
