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\input{"/Labs/middle_header.txt"}
\chead {BayesLabs}
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\begin_layout Section*
Instructions for Using BayesLabs
\end_layout

\begin_layout Standard
This repository contains a series of laboratory exercises designed to support
 an introductory course in Bayesian modeling for graduate students, post-docs,
 faculty, and research scientists.
 The material can be adapted for use in a 10-day intensive workshop format
 or a more traditional semester-long course.
 Examples are drawn from social science and ecology.
 The materials were developed by N.
 Thompson Hobbs, Mevin B.
 Hooten, Christian Che-Castaldo, Mary Collins, Kiona Ogle, and Maria Uriarte
 with support from the National Science Foundation (awards DBI 1052875 and
 DEB1145200).
 The labs were designed to compliment reading and lectures based on Hobbs,
 N.
 T., and M.
 B.
 Hooten.
 2015.
 Bayesian models: a statistical primer for ecologists.
 Princeton University Press, Princeton, N.J.
 (Table 1).
\end_layout

\begin_layout Standard
Each folder in the repository contains an R markdown file with switches
 that toggle output of .html files for exercises alone and exercises with
 answers.
 Using these switches is explained in the R markdown files.
 The files 
\family typewriter
/Labs/title.txt 
\family default
and 
\family typewriter
/Labs/subtitle.txt 
\family default
specify the course name on all materials, allowing users to change the title
 to match the name of the course they are teaching.
 We ask that the logo acknowledging NSF support remain unchanged by users.
 
\end_layout

\begin_layout Standard
The 
\family typewriter
/Admin 
\family default
folder contains instructions for pulling the repository and for creating
 an R package containing the data library required for the exercises.
\end_layout

\begin_layout Standard
\begin_inset Float table
placement H
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Laboratory exercises supporting an introductory course in Bayesian modeling.
 Readings are chapters from Hobbs and Hooten 2015.
 Exercises are arranged with introductory topics at the top of the table
 and more advanced topics toward the bottom.
\end_layout

\end_inset


\end_layout

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\begin_inset Tabular
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Folder in 
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/Labs
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</cell>
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\begin_layout Plain Layout
Topics and challenges
\end_layout

\end_inset
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\begin_inset Text

\begin_layout Plain Layout
Reading
\end_layout

\end_inset
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\begin_inset Text

\begin_layout Plain Layout

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Probability
\end_layout

\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Rules of probability.
 Factoring joint distributions.
 Probability distributions.
 Marginal distributions.
 Moment matching.
\end_layout

\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Chapter 3
\end_layout

\end_inset
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\begin_inset Text

\begin_layout Plain Layout

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HemlockLightExample
\end_layout

\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Likelihood functions.
 Computing total likelihoods from multiple observations.
 Using prior information in the likelihood framework.
\end_layout

\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Chapter 4
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\end_inset
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ConjugatePriors
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\end_inset
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\begin_layout Plain Layout
Find parameters of posterior distributions using conjugacy between likelihoods
 and priors.
 
\end_layout

\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Chapter 5.3
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\end_inset
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\family typewriter
BayesTheorem
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\end_inset
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\begin_layout Plain Layout
Compute the likelihood, prior, and marginal distribution of the data and
 assemble them to compute the posterior distribution.
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\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Chapter 5.1, 5.2
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\end_inset
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MCMC1
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\end_inset
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\begin_inset Text

\begin_layout Plain Layout
Introduction to Markov chain Monte Carlo using Gibbs sampling for normal
 mean and variance.
\end_layout

\end_inset
</cell>
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\begin_layout Plain Layout
Chapter 7
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MCMC2
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Accept-reject sampling using Metropolis-Hastings algorithm.
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\begin_layout Plain Layout
Chapter 7
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JAGSPrimer
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A tutorial on JAGS and rjags for implementing Markov chain Monte Carlo.
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none
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Using JAGS and rjags.
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none
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ModelBuilding
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\begin_layout Plain Layout
Word problems to teach drawing directed acyclic graphs and using them to
 write posterior and joint distributions.
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\end_inset
</cell>
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\begin_inset Text

\begin_layout Plain Layout
Chapters 6, 10, 11, 12
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\end_inset
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MultiLevelModels
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Model building and computation for group-level intercepts and slopes.
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Chapter 6.2.2
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Using data simulation to test for lack of fit.
 Computing Bayesian p values.
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Chapter 8.1
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Occupancy models.
 Zero inflation.
 Derived quantities.
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Chapter 6.2.3
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ModelSelection
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Evaluating evidence for alternative models using the DIC, WAIC, and posterior-pr
edictive loss.
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\end_inset
</cell>
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\begin_layout Plain Layout
Chapter 9
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DynamicModels
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\begin_layout Plain Layout
State space models.
 Predictive process distributions.
 Forecasting.
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\end_inset
</cell>
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Chapter 8.4, 8.5
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MetaAnalysis
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Combining information from multiple studies in priors and likelihoods.
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</cell>
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Chapter 4
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\end_body
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