Structures and routines that facilitate more convenient code solutions to problems of inference and smoothing for diffusion processes.
Key Features • Installation • How To Use • Related • License
- Structs that gather all containers needed for inference and smoothing of diffusions in one place
- Structs that provide views into structs above i.e. facilitate use of the blocking technique
- Structs that provide a translation between
- names of the parameters as known by the Markov chain updating parameter values
- and names of the parameters as known by the diffusion laws or observations
- Multiple methods for all of the above
⚠️ This package is in an early development stage. In particular, some functionality may depend on code in repositories DiffusionDefinition.jl, ObservationsSchemes.jl or GuidedProposals.jl that I haven't pushed to those repos yet. I would suggest not to use DiffusionMCMCTools.jl until this message disappears!
The package is not yet registered. To install it, type in:
] add https://github.com/JuliaDiffusionBayes/DiffusionMCMCTools.jlSee the documentation.
DiffusionMCMCTools.jl belongs to a suite of packages in JuliaDiffusionBayes, whose aim is to facilitate Bayesian inference for diffusion processes. Some other packages in this suite are as follows:
- DiffusionDefinition.jl: define diffusion processes and sample from their laws
- 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