This repository contains code related to the method we have proposed in our NeurIPS 2021 paper

P Rodrigues, T Moreau, G Louppe, A Gramfort "HNPE: Leveraging Global Parameters for Neural Posterior Estimation".

We have included basic code for reproducing the two numerical illustrations presented in the paper:

• A toy model for which we can derive all analytic properties of the posterior distribution
• The Jansen-Rit neural mass model that we have used to relate physiological parameters to real EEG signals

Most of our code is based on the wonderful sbi package, available here: https://github.com/mackelab/sbi

Clone the github repository on your local computer, and install the package by running:

python setup.py develop

All dependencies are listed in requirements.txt for your interest.

To ensure that your code finds the right scripts, open a python shell and type:

import hnpe

Note that you might want to create a virtual environment before doing all these installations, e.g.:

conda create -n hnpe_env

Important: To run the examples in Ex2-JRNMM you will need to make sure that the R code in https://github.com/massimilianotamborrino/sdbmpABC runs on your computer! This is a C++ implementation of the Jansen-Rit neural mass model compiled for R and which we bind to python.

To obtain an approximation of the posterior distribution of our toy model when only one observation is available and no noise is added to it, you should go to Ex1-ToyModel and enter in your terminal

python inference.py

This creates an approximation of the posterior distribution and stores its parameters in /results

To check the results, you can simply enter

python inference.py --viz

Now if you would like to see what happens to the posterior distribution when N = 10 extra observations are available, you should enter

python inference.py --nextra 10

To include noise in the observations, you should run, for instance,

python inference.py --nextra 10 --noise 0.05

The way of doing things for Ex2-JRNMM is exactly the same, except for a few choices of input parameters.

Please do

python inference.py --help

for a list of all options available for each example.