by gholtgrieve, last updated 8/6/13, sharing set to public
BaMM (for Bayesian Metabolic Model) is a Bayesian statistical model of oxygen dynamics which accounts for the dominant physical and biological processes that control dissolved oxygen in aquatic ecosystems. Using this model is it possible to estimate ecosystem metabolic rates (gross primary production, community respiration, gas exchange) from diel data of oxygen concentration and, if available oxygen-18 isotopes.
Please see the instructions document for a description on how to work with BaMM.
by eric.ward, last updated 11/20/09, sharing set to public
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.
by brice.semmens, last updated 8/23/07, sharing set to public
This program allows one to walk through the steps required to conduct a population viability analysis, or PVA, using a population time time series. The model outputs probabilities of extinction as a function of time steps into the future, and importantly, gives confidence intervals for these probabilities.
This tool has two major advantages over traditional PVA techniques:
1) It uses a state-space Kalman filter that allows for both process and non-process error.
So what's the big deal? --Functionally it filters the data, and allows a more accurate fit for population parameters of interest.
2) It uses a Bayesian sampling-importance-resampling algorithm to fully address uncertainty in the parameter estimates given the data.
So what's the big deal? -- Rather than developing a single function that describes the probability of population extinction through time, we can use the uncertainty in parameter estimates to develop 'probabilities of probabilities', or, the uncertainty surrounding the probability of extinction through time.
by eric.ward, last updated 5/19/10, sharing set to public
This project is related to the stable isotope mixing models that include heterogeneity in individual diets and hierarchical nested structures (individuals in groups, groups in regions, etc). All code provided is to be used with R and either JAGS or BUGS (everything was validated using JAGS on a Mac)
by brice.semmens, last updated 10/3/12, sharing set to public
MixSIR is a Bayesian isotope mixing model that incorporates uncertainty in the estimates of mix and source isotope values. The model also provides the opportunity to incorporate prior information for the proportional contribution of each source to the mix. The programs for this project are written in Matlab. I have provided the source files as well as an .exe file package so that those without Matlab can run the program. Collaborations and improvements are welcome! Prior to installing MixSIR you MUST download the (free) Matlab component runtime library and run it once on your machine. Note also that installing new versions of MixSIR will require a new download and installation of the component runtime library.
***** NOT WORKING ON YOUR NEW 64 BIT MACHINE?? *****
-- You need to install this:
you're runnning a 32-bit compiled Matlab app on a 64-bit Windows machine, and it's not finding the 32-bit Visual C++ runtime it's linked against. The Microsoft software "Microsoft Visual C++ 2005 SP1 Redistributable Package (x86)" will fix the problem. Cut and paste the link above to get the software fix!