Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion

This repository contains code that demonstrate the approximation of a SDE by an ODE for inference, supporting the above paper.

Dependencies

Generic scientific Python stack: numpy, scipy, matplotlib, sklearn, seaborn, joblib, and arviz (0.4.1).

To install NumPyro read the following: http://num.pyro.ai/en/stable/getting_started.html#installation

Usage

To run the stochastic Lotka-Volterra model: python lotkavolterra_example.py

By default the number of particles for PMMH is set to 100, to change this run with option: python lotkavolterra_example.py --pmmh_nparticles 100