Sample a repelled point process and compute a Monte Carlo estimation for the integral of a function using various variants of the Monte Carlo method, including the Monte Carlo with a repelled point process.

• Monte Carlo with the repelled point processes (MCRPPy)
  • Introduction
  • Dependencies
  • Installation
    • Install the project as a dependency
    • Install in editable mode and potentially contribute to the project
  • How to cite this work
    • Companion paper
    • Citation

mcrppy is an open-source Python project that currently includes methods for sampling from a variety of point processes, including the homogeneous Poisson, Thomas, Ginibre, Scrambled Sobol, Binomial, and their repelled counterparts. The project also includes several variants of the Monte Carlo method, including Monte Carlo with a repelled point process. This project serves as a companion code for the research paper titled Monte Carlo with the repelled Poisson point process; see: How to cite this work.

• mcrppy works with .

• Python dependencies are listed in the pyproject.toml file.

• Install from source (this may be broken)

  # activate your virtual environment and run
  poetry add git+https://github.com/dhawat/MCRPPy.git
  # pip install git+https://github.com/For-a-few-DPPs-more/MCRPPy.git

The package can be installed in editable mode using poetry.

To do this, clone the repository:

• if you considered forking the repository

  git clone https://github.com/your_user_name/MCRPPy.git

• if you have not forked the repository

  git clone https://github.com/dhawat/MCRPPy.git

and install the package in editable mode

cd mcrppy
poetry shell  # to create/activate local .venv (see poetry.toml)
poetry install
# poetry install --no-dev  # to avoid installing the development dependencies
# poetry add -E docs -E structure-factor  # to install extra dependencies

We wrote a companion paper to mcrppy, "Monte Carlo with the repelled Poisson point process". In the paper, we introduced the repelled point process, analyzed its properties, and utilized it to develop a variance reduction Monte Carlo method (MCR). We also conducted a comparison study of MCR against other competing Monte Carlo methods.

If mcrppy has been significant in your research, and you would like to acknowledge the project in your academic publication, please consider citing it with this piece of BibTeX::

@article{HBLR2023,
  arxivid = {TBC},
  journal = {TBC},
  author  = {Hawat, Diala and Bardenet, R{\'{e}}mi and Lachi{\`{e}}ze-Rey, Rapha{\"{e}}l},
  note    = {TBC},
  title   = {Monte Carlo with the repelled Poisson point process},
  year    = {2023},
}