Torch-MvNorm

Torch-MvNorm is a small Python package that allows

• Multivariate normal density integration, in particular computing cumulative distribution functions (CDFs)
• Partial differentiaton of CDFs through implementation of closed-form formulas (see e.g. Marmin et al. 2019, appendix 6)
• Quantities manipulation within PyTorch tensor-based framework

---

• About Torch-MvNorm
• Installation
• Getting Started
• Communication and contribution

About Torch-MvNorm

Torch-MvNorm is a library that consists of the two following components:

• mvnorm.fotran_interface -- PyTorch-Fortan bridge for Alan Genz's routine
• mvnorm.autograd -- implementation of the formula of the multivariate normal CDF gradient

Installation

Dependencies

• Install PyTorch

• Install gfortran and python-dev

sudo apt-get update -y
sudo apt-get install gfortran
sudo apt-get install python-dev

• Install joblib python modules

sudo apt-get install -y python3-joblib

• Check in your python3 console from Cython.Distutils import build_ext. If Cython is not already installed, you can try:

python3 -m pip install Cython

Get the Torch-MvNorm source

git clone --recursive https://github.com/SebastienMarmin/torch-mvnorm
cd torch-mvnorm

Install Torch-MvNorm

Compile Fortran and build the interface:

python3 setup.py build_ext --inplace

Test the code

python3 tests/test_general.py

Getting Started

• Run the code on small examples.
• Have a look at the documentation.

Communication and contribution

I welcome all contributions. Please let me know if you encounter a bug by filing an issue. Feel free to request a feature, make suggestions, share thoughts, etc, using the GitHub plateform or contacting me.

If you came across this work for a publication, please considere citing me for the code or for the mathematical derivation.

License

Torch-MvNorm is under GNU General Public License. See the LICENSE file.