Implementation of Guided Proposals (introduced by M Schauer, F van der Meulen, H van Zanten)
Author JuliaDiffusionBayes
1 Star
Updated Last
3 Years Ago
Started In
March 2020


Defining and sampling conditioned diffusion processes. A member of the suite of packages from JuliaDiffusionBayes.

Stable Dev Build Status

Key FeaturesInstallationHow To UseRelatedLicense

Key features

Implementation of the Backward Filtering–Forward Guiding (BFFG) algorithm. It is a generic computational framework for working with Guided Proposals introduced by M Schauer, F van der Meulen and H van Zanten [arXiv].

The main object introduced by this package is a struct GuidProp and it allows for sampling of guided proposals, computing log-likelihood functions of the sampled trajectories and embedding the samplers in smoothing or inference algorithms.


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How To Use

See the documentation.


GuidedProposals.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
  • ExtensibleMCMC.jl: a modular implementation of the Markov chain Monte Carlo (MCMC) algorithms
  • DiffusionMCMCTools.jl: utility methods that facilitate easier coding solutions for smoothing and inference algorithms for diffusions
  • DiffusionMCMC.jl: Markov chain Monte Carlo (MCMC) algorithms for doing inference for diffusion processes