Statistical Inference for Partially Observed Markov Processes via the R Package pomp

@article{King2015StatisticalIF,
  title={Statistical Inference for Partially Observed Markov Processes via the R Package pomp},
  author={A. King and D. Nguyen and E. Ionides},
  journal={Journal of Statistical Software},
  year={2015},
  volume={69},
  pages={1-43}
}
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… Expand
Simulation-based inference methods for partially observed Markov model via the R package is2
Accelerate iterated filtering
Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter
Stochastic Epidemic Models inference and diagnosis with Poisson Random Measure Data Augmentation.
Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 80 REFERENCES
Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
Particle Markov chain Monte Carlo methods
Particle Markov chain Monte Carlo
Time series analysis via mechanistic models
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Inference for nonlinear dynamical systems
Statistical Software for State Space Methods
Iterated filtering
Combined Parameter and State Estimation in Simulation-Based Filtering
  • Jane Liu, M. West
  • Computer Science
  • Sequential Monte Carlo Methods in Practice
  • 2001
...
1
2
3
4
5
...