We continue using material from the short course Simulation-Based Inference for Epidemiological Dynamics (SBIED). Chapter 14 is Lesson 4 of SBIED. The main topic is likelihood maximization via an iterated particle filter. This enables a range of tools of likelihood-based inference to be applied—maximum likelihood estimation, likelihood ratio tests, profile likelihood confidence intervals, and AIC for model selection. Methods are demonstrated on a model for measles, but these techniques apply to the wide range of POMP models for which particle filtering is applicable.
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| Recording, Chapter 14, Sections I,II | Classification of inference methods for POMP models | (16 mins) |
| Recording, Chapter 14, Section III | Introduction to iterated filtering | (15 mins) |
| Recording, Chapter 14, Section IV | Iterated filtering in practice | (25 mins) |
| Recording, Chapter 14, Sections V | Global likelihood maximization and profile likelihood | (33 mins) |
| Recording, Chapter 14, Section VI | Using likelihood and profile calculations to develop a data analysis | (20 mins) |
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