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