Stan Circular Inference

Author: Diogo Almeida - fc64854@alunos.ciencias.ulisboa.pt

Introduction

This work is motivated by the gap between circular statistical theory and its practical application in the context of probabilistic programming. The project focuses on the specification and estimation of circular models using Bayesian inference, and on building a modular and intuitive Python library.

The library will provide algorithms to describe various models based on circular distributions (including the following distributions: Von Mises, Cardioid, Wrapped Cauchy), and will use an existing probabilistic programming tool from the literature, Stan. This library is a public service, organized, tested, validated, and well-documented, making these models accessible to a wider audience.

The project is essentially divided into two modules: the Inference Pipeline and the Probabilistic Models.

Step by Step Setup

To obtain a stable environment for using the tool, the user is recommended to follow the steps below.

On Windows environments, execute the following commands:

wsl --install (requires to restart)
wsl -d Ubuntu

To obtain a stable version of Anaconda:

sudo wget https://repo.continuum.io/archive/Anaconda3-2022.10-Linux-x86_64.sh
bash Anaconda3-2022.10-Linux-x86_64.sh (requires to restart)
# Do you wish the installer to initialise Anaconda3? yes

Close the command line and open it again.

On Windows systems, to enter the Linux subsystem:

wsl -d Ubuntu

To install the Ubuntu dependencies:

sudo apt update
sudo apt install build-essential
sudo apt-get install manpages-dev

To create an environment in Anaconda:

conda create -n environment_name python=3.10

To activate that environment:

conda activate environment_name

To install the Anaconda dependencies:

conda install -c conda-forge gcc libstdcxx-ng

Finally, to install the tool's dependency:

pip install stan-circular-inference

Inference Pipeline

The specifics of the service module are documented separately in service.

Probabilistic Models

A comprehensive description of the xpto module is provided in xpto.