Structured way of defining diffusion processes
Author JuliaDiffusionBayes
2 Stars
Updated Last
2 Years Ago
Started In
March 2020


A collection of convenient methods for defining diffusion processes and sampling from their laws.

Stable Dev Build Status

Key FeaturesInstallationHow To UseRelatedLicense

Key Features

  • Convenient methods facilitating
    • defining diffusion laws
    • forward-sampling their trajectories
    • computing functionals of sampled paths
    • computing gradients of functionals of sampled paths with respect to diffusion parameters or with respect to the starting point of the trajectory
  • A number of predefined diffusion processes that can be immediately loaded in and experimented on


] add DiffusionDefinition

How To Use

See the documentation.


DiffusionDefinition.jl belongs to a larger suite of packages in JuliaDiffusionBayes designed to facilitate Bayesian inference for diffusion processes. Other packages in this suite consist of:

  • ObservationSchemes.jl: a systematic way of encoding discrete-time observations for stochastic processes
  • GuidedProposals.jl: defining and sampling conditioned diffusion processes
  • ExtensibleMCMC.jl: a modular implementation of the Markov chain Monte Carlo (MCMC) algorithms
  • DiffusionMCMC.jl: Markov chain Monte Carlo (MCMC) algorithms for doing inference for diffusion processes