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   Statistical inference for partially observed markov processes via the R package pomp  
   
نویسنده king a.a. ,nguyen d. ,ionides e.l.
منبع journal of statistical software - 2016 - دوره : 69 - شماره : 0 - صفحه:1 -43
چکیده    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. © 2016,american statistical association. all rights reserved.
کلیدواژه Hidden markov model; Markov processes; Maximum likelihood; Mechanistic model; Plug-and-play; R; Sequential Monte Carlo; State space model; Stochastic dynamical system; Time series
آدرس departments of ecology and evolutionary biology and mathematics,center for the study of complex systems,university of michiganmi 48109, United States, department of statistics,university of michiganmi 48109, United States, department of statistics,university of michiganmi 48109, United States
 
     
   
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