Introduction to Pomp: Inference for Partially-observed Markov Processes
نویسندگان
چکیده
1. Partially-observed Markov processes 1 2. A first example: a discrete-time bivariate autoregressive process. 3 3. Defining a partially observed Markov process in pomp. 3 4. Simulating the model 5 5. Computing likelihood using particle filtering 6 6. Interlude: utility functions for extracting and changing pieces of a pomp object 9 7. Estimating parameters using iterated filtering: mif 10 8. Nonlinear forecasting: nlf 11 9. Trajectory matching: traj.match 12 10. A more complex example: a seasonal epidemic model 14 References 18
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