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 DiffusionDefinitionSee 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