Julia package SDE.jl


The main module

Simulate diffusion processes in one or more dimension. Especially simulate vector linear processes / Ornstein-Uhlenbeck processes Monte Carlo sample diffusion bridges, diffusion processes conditioned to hit a point v at a prescribed time T Functions for transition density, mean and covariance of linear processes Perform Monte Carlo estimates of transition densities of general diffusion processes

with a Submodule

To nonparametrically estimate the drift of a diffusion with unit diffusion coefficient using a Schauder wavelet “basis”

The package contains the additional modules:

Alternative API to generate Ito processes and diffusions
Random symmetric, positive definite, stable matrix for testing purposes.


The producure in SDE.Schauder is a Julia implementation of nonparametric Bayesian inference for “continuously” observed one dimensional diffusion processes with unit diffusion coefficient. The drift is modeled as linear combination of hierarchical Faber–Schauder basis functions with a Gaussian prior on the coefficients. This incorporates a Brownian motion like prior on the drift function. The posterior is then computed using Gaussian conjugacy.

This is work in progress.

Indices and tables