Model-based Inference in Ecology and Epidemiology.
Ecological and epidemiological systems are particularly interesting from the physical point of view. Their complexity and high-dimensionality makes it natural to approach them as stochastic, nonlinear dynamical systems and within this context, many questions of both intrinsic interest and practical concern can be formulated. To answer these questions, it is necessary to rigorously confront hypothetical models with data. In this regard, time-series data are of particular value inasmuch as they have the potential to express the characteristic signatures of causal mechanisms. This course will take students into the heart of these issues via an introduction to ecological and epidemiological stochastic dynamical systems models using a series of examples with real data. Students will learn how to formulate questions as models and answer the questions using state-of-the-art inference algorithms.
- to introduce partially observed Markov process (POMP) models as tools for scientific investigation
- to give students the ability to formulate POMP models of their own
- to teach efficient approaches for performing scientific inference using POMP models
- to familiarize students with the pomp package
- to give students opportunities to work with such inference methods
- to provide documented examples for student re-use
- Familiarity with deterministic dynamics (discrete-time maps, ordinary differential equations) and probability.
- Some programming experience, in any language.
- Completion of the R tutorial before the beginning of the course.
- A sense of humor.
Format and expectations
The course will be taught using a mixture of lectures and computational laboratory exercises. Students are expected to complete assigned readings before class meetings, keep up with assigned homework, participate fully in discussions, and work on course activities during class meetings.