This repository contins two Jupyter notebooks that illustrates how to perform maximum likelihood estimation using Jax.
The notebooks are differentiated by how the likelihood function is coded.
The notebook was inspired by the following blog post from Rob Hicks: Using Autograd for Maximum Likelihood Estimation. I encourage interested individuals to read the blog post before trying to understand the notebook.
The following StackOverflow post should also be read to understand some of the changes in the function that evaluates the likelihood function.
Please note that Jax currently only works with Linux and Mac OSX. However, there are instructions on how to install Jax on Windows in this Reddit post: