Generalized Linear Mixed‐effects Model in Python
or the many ways to perform GLMM in python playground
Whenever I try on some new machine learning or statistical package, I will fit a mixed effect model. It is better than linear regression (or MNIST for that matter, as it is just a large logistic regression) since linear regressions are almost too easy to fit. Hence this collection of codes that all doing (more or less) the same thing.
Estimate uncertainty related to model parameter using dropout in Theano and TensorFlow
DROPOUT AS A BAYESIAN APPROXIMATION
K-Fold Cross Validation and Leave-One-Out (LOO)
WAIC and cross-validation in Stan