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CCheCastaldo committed May 13, 2019
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---
output:
html_document:
highlight: kate
theme: paper
---

<br>

#### Course Preparation

R is a crucial skill for success in this course. Considering reviewing Tom's R Primer, and head over to [Quick-R](http://www.statmethods.net/index.html), which gives a nice overview of basic R functionality. We ask that you are fluent with the following topics from Quick-R:

1. Data Types, Importing Data (from excel or a .csv file), Keyboard Input, and Missing Values under Data Input
2. All topics under Data Management
3. All topics under Graphs

Specifically, you should be very comfortable with manipulating matrices and lists and writing and using custom functions.

We strongly recommend purchasing [Hobbs & Hooten 2015](https://www.amazon.com/Bayesian-Models-Statistical-Primer-Ecologists/dp/0691159289). The first three chapters provide foundational material that we will cover fairly quickly in the course, so if you have not had a course in mathematical statistics, reading those chapters before the course is crucial. The structure of the course closely follows the organization of the book and it will be a useful reference after the course.

<br>

#### Course Logistics

We will be starting at 9am each day. We will usually end at 5 or 5:30.

Remember that lunch will be served at SESYNC each day during the course.

Course materials will be distributed throughout the course via [GitHub](https://github.com/CCheCastaldo/SESYNCBayes).

<br>

#### Install course-specific R package `SESYNCBayes`

We have prepared an R package that contains all the data you will need to complete our lab exercises. The package is part of the course materials that you now have on your local machine. You will need to do an initial install of this package and do periodic updates throughout the course. For both the install and update the commands are the same.

1. Open R or RStudio run the following line of code to install the `SESYNCBayes` package from source. Remember to change `<pathtoSESYNCBayes>` to the path to the directory where you cloned the SESYNCBayes course repository.

``` {r, eval = FALSE, echo = TRUE, include = TRUE}
install.packages("<pathtoSESYNCBayes>/Packages/SESYNCBayes_0.2.0.tar.gz", repos = NULL, type = "source")
```

2. When working in RStudio you load the library like any other R library:

``` {r, eval = FALSE, echo = TRUE, include = TRUE}
library(SESYNCBayes)
```

3. It is easy to see the help files for `SESYNCBayes` type in R:

``` {r, eval = FALSE, echo = TRUE, include = TRUE}
?SESYNCBayes
```

4. Here is how to load a dataset from `SESYNCBayes`. For example:

``` {r, eval = FALSE, echo = TRUE, include = TRUE}
data(LynxFamilies)
```


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---
output:
html_document:
highlight: kate
theme: paper
---

<br>

##### **Day 1: (6/3) Probability**

1. Participant and Course Introduction
2. [What Sets Bayes Apart?](content/lectures/WhatSetsBayesApart.pdf)
3. Confidence intervals defined
4. Laws of Probability & [Probability Lab I-V](content/labs/LawsProbability.html)
5. Probability Concepts and Notation & Probability Lab VI
6. Probability Distributions & Probability Lab VII

Review Hobbs and Hooten chapters 1-3.

<br>

##### **Day 2: (6/4) Likelihood & Bayes' Theorem**

1. Marginal Distributions & Probability Lab VIII
2. Moment Matching & Moment Matching Probability Lab
3. Likelihood & Likelihood Problem Set

Review Hobbs and Hooten chapters 4-6.

<br>

##### **Day 3: (6/5) Intro to Bayesian Statistics**

1. Bayes' Theorem & Bayes' Theorem Lab
2. More about Priors & Priors Lab
3. MCMC Overview & MCMC Lab (optional)


<br>

##### **Day 4: (6/6) Markov Chain Monte Carlo**

1. JAGS Primer Work & Exercises
2. Inference From a Single Model
3. Islands Lab
4. Happy Hour at SESYNC

Review Hobbs and Hooten chapter 7.

<br>

##### **Day 5: (6/7) Modeling Practice**

1. Bayesian Regression & Lab
2. Vague Priors in Non-Linear Models
3. Designed Experiments & Lab

Review Hobbs and Hooten chapter 8.

<br>

##### **Day 6: (6/8) Off**

<br>

##### **Day 7: (6/9) Bayesian Regression & Multi-Level Modeling**

1. Multi-Level Modeling & Multi-Level Modeling Lab

Review Hobbs and Hooten chapters 9-12.

<br>

##### **Day 8: (6/10) Multi-Level Modeling, Model Checking, & Designed Experiments**

1. Introduction to Hierarchical Models & Hierarchical Modeling Board Work
2. Model Checking
3. Multi-Level Modeling Lab Revisited


<br>

##### **Day 9: (6/11) Advanced Topics**

1. Dynamic Models & Lynx Lab
2. Ordinal Modeling Lab
3. Mixture Models, Zero Inflation, & Occupancy & Swiss Birds Lab
4. Model Selection Lab Lab

<br>

##### **Day 10: (6/12) Spatial Modeling & Individual Projects**

1. Participant Project Work
2. Spatial Modeling & Lab
3. Bayesian Modeling Practicalities

<br>

##### **Day 11: (6/13) Presentation Day (ALL DAY)**

1. Course Evaluation Survey
2. Participant Project Presentations and Feedback
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