DPOMPs.jl

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.

Features

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

Algorithms

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

• Data-augmented MCMC
• Particle filters
• Iterative-batch-importance sampling

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/DPOMPs.jl

Usage

The package documentation has more information and examples.