Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

This repository is a supplement to Coventry and Bartlett's tutorial on using Bayesian methods in neuroscience studies using PyMC.

OS recommendations

This code works for Windows and Linux distributions. We believe this should work for MacOS, but is untested.

Setting up a PyMC environment

To use these scripts, we strongly recommend setting up an Anaconda environment dedicated to PyMC. Anaconda can be freely downloaded directly from https://www.anaconda.com/. To setup a PyMC environment, open an anaconda prompt and run:

conda create -n PyMC python=3.8

which creates a conda environment named PyMC using Python version 3.8. We recommend at least this version of Python, but have tested down to Python 3.6.8 as working. Anaconda environments create self constrained python libraries such that Python dependencies from one environment are not altered by changed in other project environments.

To activate an environment, in anaconda, run

conda activate PyMC

Installing PyMC and required packages

To install PyMC, first activate a PyMC environment. Then run:

conda install -c conda-forge "pymc>=5"
conda install -c conda-forge numpyro
conda install m2w64-toolchain
pip install arviz
pip install aesara
pip install numpy
pip install pandas
pip install scipy
pip install seaborn
pip install matplotlib

This installs PyMC and all required packages individually. Alternatively, pip installations can be groups as "pip install scipy,numpy,..."

To run DBS LFP models, install the following packages:

pip install tabulate
pip install docopt
pip install nutpie

Running sample programs

We recommend creating a new directory on your machine to download all programs and data to. To run programs, activate the PyMC environment and navigate to the code/data directory as:

cd 'path to your directory'

An example of 'path to your directory might be 'C:\CodeRepos\PyMC'.

Programs are then run by simplying typing

python BayesRegression.py

Programs will run and give diagnostics in the anaconda terminal and show plots found in the paper.

Where do I find the data?

Data for Bayesian linear regressions, comparisons of models, and BANOVA/BANCOVAs are found at the following open science framework link: Data for Bayesian Multilinear regressions can be found at its papers open science framework repository: DOI 10.17605/OSF.IO/W4UFH

Batch Processing

For an example of running models in batches, see batch_BHLR.py which does a sensitivity sweep. To keep things simple, this is a simple loop function. More fancy MPI based processes can be done. However, this would take a good amount of work to work between threads already used by PyMC. This should run plenty fast as is.

Potential Errors

A common error currently installing pymc is a 'distutils not found' error. This is because distutils was moved for Python >= 3.12. A way to fix this is

conda install "setuptools <65"

in the anaconda environment.