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Generalized Linear Mixed‐effects Model in Python

or the many ways to perform GLMM in python playground

A comparison among:
StatsModels
Theano
PyMC3(Base on Theano)
TensorFlow
Stan and pyStan
Keras
edward

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.

TODO

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
tyarkoni/PPS2016

More information (codes) could be found below (to name a few):

paul-buerkner/brms
vasishth/BayesLMMTutorial
jonsedar/pymc3_vs_pystan
Example from PyMC3
tyarkoni/nipymc
bambinos/bambi

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