DiffusionDefinition.jl

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

Key Features • Installation • How To Use • Related • License

• 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

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

MIT