pomp2-package: Inference for partially observed Markov processes

In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description

The pomp2 package provides facilities for inference on time series data using partially-observed Markov process (POMP) models. These models are also known as state-space models, hidden Markov models, or nonlinear stochastic dynamical systems. One can use pomp2 to fit nonlinear, non-Gaussian dynamic models to time-series data. The package is both a set of tools for data analysis and a platform upon which statistical inference methods for POMP models can be implemented.

Data analysis using pomp

pomp provides algorithms for

1. simulation of stochastic dynamical systems; see simulate

2. particle filtering (AKA sequential Monte Carlo or sequential importance sampling); see pfilter

3. the iterated filtering methods of Ionides et al. (2006, 2011, 2015); see mif2

4. the nonlinear forecasting algorithm of Kendall et al. (2005); see nlf

5. the particle MCMC approach of Andrieu et al. (2010); see pmcmc

6. the probe-matching method of Kendall et al. (1999, 2005); see probe.match

7. a spectral probe-matching method (Reuman et al. 2006, 2008); see spect.match

8. synthetic likelihood a la Wood (2010); see probe

9. approximate Bayesian computation (Toni et al. 2009); see abc

10. the approximate Bayesian sequential Monte Carlo scheme of Liu & West (2001); see bsmc2

11. ensemble and ensemble adjusted Kalman filters; see kalman

12. simple trajectory matching; see traj.match.

The package also provides various tools for plotting and extracting information on models and data.

Author(s)

Aaron A. King

References

A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the Package pomp. Journal of Statistical Software 69(12): 1–43. An updated version of this paper is available on the package website.

See the package website, https://kingaa.github.io/pomp/, for more references.

See Also

Other information on model implementation: Csnippet, accumulators, covariate_table, distributions, dmeasure_spec, dprocess_spec, parameter_trans, prior_spec, rinit_spec, rmeasure_spec, rprocess_spec, skeleton_spec, transformations, userdata

Other pomp parameter estimation methods: abc, bsmc2, kalman, mif2, nlf, pmcmc, probe.match, spect.match

Other elementary POMP methods: pfilter, probe, simulate, spect

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.