Systematic way of defining observation schemes for stochastic processes
Author JuliaDiffusionBayes
0 Stars
Updated Last
3 Years Ago
Started In
April 2020


A utility package from the JuliaDiffusionBayes suite, used for defining observation schemes for stochastic processes. It is aimed primarily at encoding discrete-time observations of diffusions.

Stable Dev Build Status

Key FeaturesInstallationHow To UseRelatedLicense

Key Features

  • Decorate each observation separately with the information about how it was collected
  • Support for the following observations:
    • Exact observations of all or a subset of all coordinates of the underlying process
    • Linear translations of the underlying process, disturbed by Gaussian noise: equation
    • First-passage time observations
    • First-passage time observations with additional "resetting events"
    • Non-linearly (or linearly) transformed observations with Gaussian or non-Gaussian noise, i.e.: equation with general function g and random variable ξ
    • Parameterized versions of all observation types above
  • Support for ergonomic definitions of
    • Multiple observations of a single process
    • Multiple observations of multiple processes, coming possibly from different laws that share subsets of parameters (mixed-effect models)
  • Support for defining priors over starting points:
    • Degenerate priors corresponding to fixed starting points
    • Gaussian priors


] add ObservationSchemes

How To Use

See the documentation.


ObservationSchemes.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:



Used By Packages