This organization supports modeling and inference using partially observed Markov process (POMP) models.

A core goal is the development of the pypomp Python package. This seeks inspiration from the pomp R package while incorporating automatic differentiation and parallelization using JAX.

This is a new project. The first goal is to provide a package supporting the methodology explored by Tan, K., Ionides, E. L. and Hooker, G. (2024), Accelerated inference for partially observed Markov processes using automatic differentiation, arxiv:2407.03085, based on the code for this project at zenodo.13356896

• Scientists wanting to perform data analysis on a dynamic system via partially observed Markov processes (POMP), also called state-space models (SSM) or hidden Markov models (HMM).

• Many of the expected use cases and motivating examples of this package can be found on the pomp R package bibliography page.

• Researchers wishing to develop novel inference methodology for POMP models.

• This organization is collaborative. All interested individuals are welcome to contribute to existing projects or to propose new projects.

• The organization is led by the core development team, guided by some basic democratic rules.

• Those wishing to contribute can either contact the core development team or simply propose a coding contribution via a pull request.