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Analyzing Ecological Data with Hierarchical Models — March 2016

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Analyzing Ecological Data with Hierarchical Models

Five-day Workshop: 15-18 Mar 2016
Time: 9 am – 5 pm
Location: Rosenstiel School of Marine & Atmospheric Science – University of Miami, Miami, Florida
Dr. Robert M. Dorazio
Wetland and Aquatic Research Center
U.S. Geological Survey
Gainesville, Florida 32653
Email: bdorazio@usgs.gov
Course Description:
This workshop is designed to provide wildlife scientists with the training needed to formulate and fit hierarchical models of animal abundance and occurrence to actual data sets. Most of these models can be fitted using either frequentist or Bayesian methods of analysis. However, Bayesian methods will be emphasized in the workshop because scientific interest is often focused on a model’s latent state variables or predictions (such as the abundance or occurrence of animals at sampled or unsampled locations) and computing inferences for these quantities is often difficult with frequentist methods.
The workshop begins with a description of hierarchical modeling and its use in the analysis of ecological data. This is followed by a conceptual review of the frequentist system of inference and by an introduction to Bayesian methods of analysis. Next is a description of a generic set of algorithms (Markov chain Monte Carlo) that can be used to fit all of the hierarchical models described in the workshop – and many more! Armed with these tools, participants of the workshop will learn to derive and implement simulation-based algorithms for fitting hierarchical models to different types of data (as opposed to relying on software such as BUGS or JAGS). The workshop is intended to provide substantial hands-on training. Considerable time will be devoted to the technical details of fitting hierarchical models to data and to summarizing and interpreting the results of each analysis.
As time permits, data brought to the workshop by participants will be presented and analyzed as class exercises.
Prerequisites: (1) working knowledge of the R software program (R Core Team, 2015), (2) laptop computer with R installed, (3) familiarity with the frequentist system of statistical inference (including maximum likelihood estimation), and (4) familiarity with the analytical evaluation of joint, marginal, and conditional probability density functions, particularly as they are used in specifying mixtures of distributions.



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