ObservationSchemes.jl

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.

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

• 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:
  • 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.: 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

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:

• DiffusionDefinition.jl: define diffusion processes and sample from their laws
• 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

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