Bayesian inference for Discrete state-space Partially Observed Markov Processes in Julia. See the docs:
Author mjb3
3 Stars
Updated Last
12 Months Ago
Started In
February 2021


Bayesian inference for Discrete-state-space Partially Observed Markov Processes in Julia

Documentation Package tests

This package contains tools for Bayesian inference and simulation of DPOMP models. See the docs.


  • Simulation and
  • Bayesian parameter inference for,
  • Discrete-state-space Partially Observed Markov Processes, in Julia.
  • Includes automated tools for convergence diagnosis and analysis.


  • Epidemiological modelling (e.g. SEIR models)
  • Ecology (e.g. predator-prey dynamics)
  • Many other potential use cases, e.g. physics; chemical reactions; social media.


The package implements several different customisable algorithms for Bayesian parameter inference, including:

  • Data-augmented MCMC
  • Particle filters (i.e. Sequential Monte Carlo)
  • Iterative-batch-importance sampling (e.g. 'SMC^2')

Getting started

Package installation

The package is not registered and must be added via the package manager Pkg. From the Julia REPL type ] to enter the Pkg mode, and run:

pkg> add https://github.com/mjb3/DiscretePOMP.jl


See the package documentation for instructions and examples.