Statistical Inference for Partially Observed Markov Processes via the R Package pomp
Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provides a very flexible framework for Monte Carlo statistical investigations using nonlinear, non-Gaussian POMP models. A rang...
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doaj-6727ff09435b47e097475111b8d3f8282020-11-24T20:51:07ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602016-03-0169114310.18637/jss.v069.i12989Statistical Inference for Partially Observed Markov Processes via the R Package pompAaron A. KingDao NguyenEdward L. IonidesPartially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provides a very flexible framework for Monte Carlo statistical investigations using nonlinear, non-Gaussian POMP models. A range of modern statistical methods for POMP models have been implemented in this framework including sequential Monte Carlo, iterated filtering, particle Markov chain Monte Carlo, approximate Bayesian computation, maximum synthetic likelihood estimation, nonlinear forecasting, and trajectory matching. In this paper, we demonstrate the application of these methodologies using some simple toy problems. We also illustrate the specification of more complex POMP models, using a nonlinear epidemiological model with a discrete population, seasonality, and extra-demographic stochasticity. We discuss the specification of user-defined models and the development of additional methods within the programming environment provided by pomp.https://www.jstatsoft.org/index.php/jss/article/view/2614Markov processeshidden Markov modelstate space modelstochastic dynamical systemmaximum likelihoodplug-and-playtime seriesmechanistic modelsequential Monte CarloR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aaron A. King Dao Nguyen Edward L. Ionides |
spellingShingle |
Aaron A. King Dao Nguyen Edward L. Ionides Statistical Inference for Partially Observed Markov Processes via the R Package pomp Journal of Statistical Software Markov processes hidden Markov model state space model stochastic dynamical system maximum likelihood plug-and-play time series mechanistic model sequential Monte Carlo R |
author_facet |
Aaron A. King Dao Nguyen Edward L. Ionides |
author_sort |
Aaron A. King |
title |
Statistical Inference for Partially Observed Markov Processes via the R Package pomp |
title_short |
Statistical Inference for Partially Observed Markov Processes via the R Package pomp |
title_full |
Statistical Inference for Partially Observed Markov Processes via the R Package pomp |
title_fullStr |
Statistical Inference for Partially Observed Markov Processes via the R Package pomp |
title_full_unstemmed |
Statistical Inference for Partially Observed Markov Processes via the R Package pomp |
title_sort |
statistical inference for partially observed markov processes via the r package pomp |
publisher |
Foundation for Open Access Statistics |
series |
Journal of Statistical Software |
issn |
1548-7660 |
publishDate |
2016-03-01 |
description |
Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provides a very flexible framework for Monte Carlo statistical investigations using nonlinear, non-Gaussian POMP models. A range of modern statistical methods for POMP models have been implemented in this framework including sequential Monte Carlo, iterated filtering, particle Markov chain Monte Carlo, approximate Bayesian computation, maximum synthetic likelihood estimation, nonlinear forecasting, and trajectory matching. In this paper, we demonstrate the application of these methodologies using some simple toy problems. We also illustrate the specification of more complex POMP models, using a nonlinear epidemiological model with a discrete population, seasonality, and extra-demographic stochasticity. We discuss the specification of user-defined models and the development of additional methods within the programming environment provided by pomp. |
topic |
Markov processes hidden Markov model state space model stochastic dynamical system maximum likelihood plug-and-play time series mechanistic model sequential Monte Carlo R |
url |
https://www.jstatsoft.org/index.php/jss/article/view/2614 |
work_keys_str_mv |
AT aaronaking statisticalinferenceforpartiallyobservedmarkovprocessesviatherpackagepomp AT daonguyen statisticalinferenceforpartiallyobservedmarkovprocessesviatherpackagepomp AT edwardlionides statisticalinferenceforpartiallyobservedmarkovprocessesviatherpackagepomp |
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1716802622494605312 |