Just some examples on using TensorFlow to compute MLEs.
I wanted to explore TensorFlow's versatility as a general-purpose statistical computing library by attempting to fit a range of models with gradient descent. These include mixture models (traditionally fit using EM algorithm) and random effects models (traditionally fit using explicitly-derived marginal likelihood maximization). I'll be adding more model implementations from time to time.
See the full blog post at http://kyleclo.github.io/maximum-likelihood-in-tensorflow-pt-1/.
This code was written for Python >= 3.6.0. To install dependencies, run:
pip install -r requirements.txt
after cloning the repo.