Bayesian approaches to structured population models
The interface between statistical and demographic modeling is rich and rapidly moving, as ecologists increasingly turn to data-driven population models to answer questions of basic and applied importance. General linear models form the backbone of many structured population models, and these are most commonly fit in the contexts of frequentist or Maximum Likelihood statistical frameworks. Bayesian analysis is an alternative approach that offers several important advantages (and a few disadvantages) for parameterizing structured population models from data. This workshop will cover the what, why, and how of Bayesian approaches to structured population models, specifically integral and matrix projection models. What is a Bayesian approach and how does it contrast with other methods of model construction? Why might one choose a Bayesian approach? How does one do this?
Questions about the workshop? Contact Tom Miller ([email protected])
The interface between statistical and demographic modeling is rich and rapidly moving, as ecologists increasingly turn to data-driven population models to answer questions of basic and applied importance. General linear models form the backbone of many structured population models, and these are most commonly fit in the contexts of frequentist or Maximum Likelihood statistical frameworks. Bayesian analysis is an alternative approach that offers several important advantages (and a few disadvantages) for parameterizing structured population models from data. This workshop will cover the what, why, and how of Bayesian approaches to structured population models, specifically integral and matrix projection models. What is a Bayesian approach and how does it contrast with other methods of model construction? Why might one choose a Bayesian approach? How does one do this?
Questions about the workshop? Contact Tom Miller ([email protected])