Bayesian SI mixing models

Project information

Project members
eric.ward brice.semmens


This project is a variation of stable isotope mixing models. Previous approaches have fixed source parameters (mean, variance) at their MLE estimates, and proceeded to do a Bayesian analysis of the mixture of consumer diets. This assumption is fine when sample sizes are large, but often in ecology, they are small. Incorporating this uncertainty adds additional parameters, and slightly increases the total variance of mixture estimates, but has the advantage of improving mixing and reducing bias. Often, using the traditional approach may lead to multi-modal estimates of the mixture; we demonstrate that the fully Bayesian mixing model avoids this problem. A final benefit is that it allows prior information about sources to be included. The code folder contains the code to replicate the comparison done with simulated datasets.


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- 4 items 11/20/2009 07:14 PST 1683 118 d



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