Skip to content
Generalized linear mixed-effect model in Python
Jupyter Notebook Other
Branch: master
Clone or download

Latest commit

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
misc file tide up Aug 11, 2016
.gitignore new sampler Dec 5, 2017
GLMM_in_python.ipynb rerun code Jan 8, 2018
LICENSE.md Linear mixed model Jun 29, 2016
Playground.py rerun code Jan 8, 2018
README.md Update README.md Feb 16, 2017
behavioral_data.txt Linear mixed model Jun 29, 2016
pymc3_different_sampling.ipynb update different sampling notebook Aug 17, 2018

README.md

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

You can’t perform that action at this time.