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\section{\texorpdfstring{BAYESIAN MODELS IN ECOLOGY}{BAYESIAN MODELS IN ECOLOGY}}
\label{sec:bayesian_models_in_ecology}


The application of Bayesian methods to research problems in ecology is advancing rapidly. Although training in these methods for graduate students is becoming far more available than it was even five years ago, it is still limited to a small number of institutions. There is a large, unfulfilled need for educating ecologists in the use Bayesian principles and methods. These methods 

\subsection{\texorpdfstring{GOALS}{GOALS}}
\label{sec:goals}


This is a 6-day workshop designed to accelerate learning of Bayesian methods by practicing ecological researchers. The workshop will consist of a series of compact lectures interspersed with extensive laboratory exercises chosen to engage participants in the principles and concepts introduced in lecture. The goals of the workshop are to: 
\begin{enumerate}
  \item Provide a principles-based understanding of Bayesian methods needed to train students, to evaluate papers and proposals, and to solve analysis problems in research. 
  \item Communicate the statistical concepts and vocabulary needed to foster collaboration between ecologists and statisticians. 
  \item Provide the conceptual foundations and quantitative confidence needed for self-teaching modern analytical methods. 
\end{enumerate}


\subsection{\texorpdfstring{LEARNING OUTCOMES}{LEARNING OUTCOMES}}
\label{sec:learning_outcomes}


At the end of  workshop participants will be able to:
\begin{enumerate}
  \item Explain key principles of Bayesian statistics including the concepts of posterior, marginal, and prior distributions, likelihood, conjugacy, conditioning, and the relationship among simple Bayesian, hierarchical Bayesian, and maximum likelihood methods. 
  \item Use basic statistical distributions (e.g., binomial, Poisson, normal, lognormal, binomial, beta, gamma) to write posterior and joint distributions for hierarchical Bayesian models including models of ecological processes, models of data, and random effects. 
  \item Explain how Monte Carlo Markov Chain (MCMC) methods estimate the posterior distributions of parameters. Write algorithms and computer code implementing MCMC for parameters in simple models composed in 2. 
  \item Use software implementing MCMC methods (i.e., JAGS, OpenBUGS, coda) to estimate posterior distributions of parameters and derived quantities of interest, to evaluate convergence, and to assess goodness of fit of models to data. 
  \item Understand approaches for evaluating the strength of evidence in data for alternative models of ecological processes. 
\end{enumerate}


\subsection{\texorpdfstring{Instructors}{Instructors}}
\label{sec:instructors}

\begin{itemize}
  \item \href{http://www.columbia.edu/~mu2126/}{ María Uriarte}, Columbia University
  \item \href{http://labtrop.ib.usp.br/}{Alexandre Oliveira}, Universidade de São Paulo
  \item Leandro Reverberi-Tambosi, IBUSP 
\end{itemize}


\subsection{\texorpdfstring{Schedule}{Schedule}}
\label{sec:schedule}


The course will take place at Instituto de Biociências, Universidade de São Paulo. 
\begin{itemize}
  \item 22-29 September 2015
  \item 9am to 5 pm 
  \item room 252, Department of Ecology
\end{itemize}

\begin{itemize}
  \item Schedule Details
\end{itemize}


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