pomp provides a very general realization of nonlinear partially-observed Markov processes (also known as nonlinear stochastic dynamical systems, hidden Markov models, and state space models). These are a generalization of linear state-space and hidden Markov models to nonlinear, non-Gaussian processes in either discrete or continuous time. In pomp, one can implement a model by specifying its unobserved process and measurement components; the package uses these functions in algorithms to simulate, analyze, and fit the model to data.
Currently, pomp provides support for
- basic particle filtering (AKA sequential importance sampling or sequential Monte Carlo),
- trajectory matching,
- the approximate Bayesian sequential Monte Carlo algorithm of Liu & West (2001),
- the particle Markov chain Monte Carlo method of Andrieu et al. (2010),
- approximate Bayesian computation (ABC; Toni et al. 2009)
- the improved iterated filtering method (Ionides et al. 2015),
- probe-matching methods based on synthetic likelihood (e.g., Kendall et al. 1999, Wood et al. 2010)
- the nonlinear forecasting method of Ellner & Kendall,
- the ensemble Kalman filter of Evensen (1994, 2009), and
- the ensemble adjustment Kalman filter of Anderson (2001), and
- power-spectrum-matching methods of Reuman et al. (2006).
pomp is also a platform upon which general inference algorithms for partially observed Markov processes can be implemented. We welcome contributions in the form of codes, examples, improvements to the documentation, bug reports, feature requests, and requests for help!
This website contains:
- installation instructions,
- a wide range of tutorials and examples,
- the complete reference manual online and as a PDF document,
- access to the source code,
- a facility for reporting issues with the package,
- a place for discussions with pomp users and developers,
- an extensive but necessarily incomplete bibliography of publications describing pomp methods and pomp applications. Please let the developers know if you find pomp useful and if you publish results obtained using it!
Although pomp is a mature package, it is actively maintained and new features are under development. If you come up with improvements, find bugs, or have suggestions or feature requests, please tell us about them using the package issues page.
To keep abreast of pomp news, view the pomp news blog and/or subscribe to the pomp RSS feed.
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This software has been made possible by support from the U.S. National Science Foundation (Grants #EF-0545276, #EF-0430120), by the “Inference for Mechanistic Models” Working Group supported by the National Center for Ecological Analysis and Synthesis (a Center funded by N.S.F. (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California), and by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, U.S. National Institutes of Health. |