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<html xmlns:v="urn:schemas-microsoft-com:vml"
xmlns:o="urn:schemas-microsoft-com:office:office"
xmlns:w="urn:schemas-microsoft-com:office:word"
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<head>
<meta name=Title content="Statistical Modeling IB/NRES 509">
<meta name=Keywords content="">
<meta http-equiv=Content-Type content="text/html; charset=utf-8">
<meta name=ProgId content=Word.Document>
<meta name=Generator content="Microsoft Word 14">
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<link rel=File-List href="GE509_files/filelist.xml">
<title>Statistical Modeling IB/NRES 509</title>
</head>
<body lang=EN-US link="#000000" vlink=purple style='tab-interval:35.45pt'>
<div class=WordSection1>
<p align=center style='text-align:center'><span
style='font-size:22.0pt;font-family:AlMateen;mso-fareast-
mso-hansi-font-family:AlMateen'>Applied Environmental Statistics<span
style="mso-spacerun:yes"> </span>GE 509<o:p></o:p></span></p>
<p align=center style='text-align:center'><span
style='font-size:22.0pt;font-family:AlMateen;mso-fareast-
mso-hansi-font-family:AlMateen'>Course syllabus<o:p></o:p></span></p>
</div>
<div class=WordSection2>
<p><b><span style='font-size:14.0pt'>Instructor:<o:p></o:p></span></b></p>
<p>Michael C. Dietze</p>
<p><a href="mailto:[email protected]">[email protected]</a></p>
<p>STO 457A</p>
<p>Office hours by appointment</p>
</div>
<div class=WordSection3>
<p><b><span style='font-size:14.0pt'>Goals:<o:p></o:p></span></b></p>
<p>The primary focus of this course is on probability-based statistical methods employed in the environmental, earth, and ecological sciences. Students in this class will explore a variety of statistical modeling topics from both a likelihood and Bayesian perspective, building progressively from simple models to sophisticated analyses. Students will be exposed to the concepts behind these approaches, the computational techniques to implement them, and their application to common problems in environmental science.Throughout the focus will be on how to construct statistical models that allow us to confront theory with data. The first third of the course will cover foundational concepts. The middle third will work from simple linear regression up to general linear mixed models and hierarchical models with particular emphasis on the complexities common to environmental data: heteroskedasticity, missing data, latent variables, errors in variables, and multiple sources of variability at different spatial,temporal, and taxonomic scales. The last third will cover time-series and spatial data, both of which are ubiquitous in the environmental and earth sciences. Attention throughout the course will be given to environmental applications, and in particular data and models unique (e.g. mark-recapture, matrix population models) or particularly important (e.g. kriging, CAR) to earth and environmental science.</p>
<p><b><span style='font-size:14.0pt'>Contact
hours/week:</span></b><span style='font-size:14.0pt'>
</span>Three 50-min lectures and one 2-hr computer lab</p>
<p><b><span style='font-size:14.0pt'>Prerequisites:</span></b><span
style='font-size:14.0pt'> <span style='mso-tab-count:
1'> </span></span>Introductory statistics (CAS MA115/116 or MA213/124 or equivalent)
and</p>
<p><span style='mso-tab-count:3'> </span>Calculus
I (CAS MA121 or CAS MA123 or equivalent) and</p>
<p><span style='mso-tab-count:3'> </span>Probability
(CAS MA581)</p>
<p style='margin-left:70.9pt;text-indent:35.45pt'>or consent of
the instructor</p>
<p><b><span style='font-size:14.0pt'>Course Materials:<o:p></o:p></span></b></p>
<p>Required Text: Models
for Ecological Data: An Introduction.<span style="mso-spacerun:yes">
</span>2007. James S. Clark ISBN: 9780691121789<br>
<span style='mso-tab-count:2'> </span>Book is available
at the university bookstore or can be purchased online</p>
<p>The primary text will be supplemented with PDFs of select readings from additional textbooks and the primary literature. Literature readings focus on examples of the application of statistical models in the environmental literature rather than methods papers. These “case studies” will also serve as the focus for the analysis problems in the lab component.</p>
<p>Students will also make extensive use of the following statistical software (which is freely available on the internet) in order to complete assignments:</p>
<p><span style='mso-tab-count:1'> </span>R<span
style='mso-tab-count:2'> </span><a
href="http://www.r-project.org/">http://www.r-project.org/</a></p>
<p><span style='mso-tab-count:1'> </span><span
class=SpellE>RStudio</span><span style='mso-tab-count:2'> </span>http://www.rstudio.com/</p>
<p><span style='mso-tab-count:1'> </span>BUGS<span
style='mso-tab-count:1'> </span><span style='mso-tab-count:1'> </span><a
href="http://www.openbugs.net">http://www.openbugs.net</a></p>
<p><span style='mso-tab-count:1'> </span><span
class=SpellE>GitHub</span><span style='mso-tab-count:2'> </span>http://github.com</p>
<p style='page-break-before:always'><b><span style='font-size:
14.0pt'>Grading:<o:p></o:p></span></b></p>
<p>Grading will be based on lab reports/problem sets, a
semester-long project, and four exams.</p>
<table>
<tr>
<td>Lab reports/problem sets (10 points each)</td><td>= 150</td>
</tr>
<tr>
<td>Semester project (Grad only)</td><td>= 95</td>
</tr>
<tr>
<td> project proposal (10)</td><td></td>
</tr>
<tr>
<td> model description (15)</td><td></td>
</tr>
<tr>
<td> preliminary analysis (20)</td><td></td>
</tr>
<tr>
<td> final report (50)</td><td></td>
</tr>
<tr>
<td>Exams (25, 30, 30, 30 points )</td><td> = 115</td>
</tr>
<tr>
<td>Total, Grad</td><td>= 360</td>
</tr>
<tr>
<td>Total, Undergrad</td><td>= 265</td>
</tr>
</table>
<p><b><span style='font-size:14.0pt'>Lectures/Labs<o:p></o:p></span></b></p>
<p>Please refer to the course website for the schedule of lecture/lab topics and the assigned readings that go with these. Students are expected to complete readings before class. </p>
<p><b>Lab attendance is <i><u>mandatory.</u></i></b> Lab reports will not be accepted for labs missed due to unexcused absences. Lab reports are due by the start of lab the following week and will be penalized 10%/day if turned in late. Lab materials will be made available in the GitHub repository https://github.com/mdietze/EE509. Details on what needs to be turned in will be provided with each lab.</p>
<p>You may discuss lab assignments with other students, but you each must turn in your own written report and code. </p>
<p><b><span style='font-size:14.0pt'>Semester Project (Graduate Students only)<o:p></o:p></span></b></p>
<p>The core component that separates the undergraduate from the graduate version of this course is a semester-long independent analysis and write-up. There are a number of benchmarks over the course of the semester to ensure adequate progress is being made and to provide you with feedback. A more detailed description will be provided before each task is due.</p>
<p><u>Project Proposal:</u> 1-2 pages double-spaced. Students are expected to describe the data set they intend to analyze and present the <u>scientific question</u> that motivates their analysis. Students are encouraged to make use of their own data sets for the semester project.</p>
<p><u>Model description:</u> 1-2 pages double-spaced. A brief description of <i>how</i> the data will be analyzed. Should include a mathematical specification of the process model(s), the data model, and the parameter model and a figure of how these relate to one another.</p>
<p><u>Preliminary Analysis:</u> 1-3 pages double space text <i>plus</i> R/BUGS code <i>plus</i> a minimum of 5 <b><i>results</i></b> figures with legends. At this point analysis should
be mostly complete. Text should briefly describe the computational methods of the analysis and any modifications of the model description (i.e. what did you actually end up doing).</p>
<p><u>Final Report:</u> The final report should be written <u>in the style and tone of a scholarly publication</u>, though with greater emphasis on the results and statistical methods employed and less on introduction and discussion. Specifically, we will be using the <i>Ecology Letters</i> format: no more than 5000 words in length and no more than 6 figures or tables. For more detailed guidelines see sections 8 and 9 of http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291461-0248/homepage/ForAuthors.html</p>
<p>Project Due Dates:</p>
<p>Project Proposal<span style='mso-tab-count:4'> </span><span
style='mso-tab-count:1'> </span>10/1</p>
<p>Model Description<span style='mso-tab-count:4'> </span>10/29</p>
<p>Preliminary Analysis<span style='mso-tab-count:4'> </span>11/19</p>
<p>Final Report<span style='mso-tab-count:4'> </span><span
style='mso-tab-count:1'> </span>Before Exam 4</p>
<p><b><span style='font-size:14.0pt'>Exams<o:p></o:p></span></b></p>
<p>Exams will be a combination of short answer and multiple choice. The final exam will be non-cumulative.</p>
<p>Midterm I<span style='mso-tab-count:5'> </span>9/19</p>
<p>Midterm II<span style='mso-tab-count:5'> </span>10/14</p>
<p>Midterm III<span style='mso-tab-count:5'> </span>11/10</p>
<p>Final<span style='mso-tab-count:6'> </span>Final
Exam period</p>
<p><b><span style='font-size:14.0pt'>Lecture Schedule<o:p></o:p></span></b></p>
<table border=1 cellspacing=0 cellpadding=0 width=698>
<tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'>
<td width=39><p>Date</p></td>
<td width=330><p>Topics</p></td>
<td width=266><p>Reading</p></td>
<td width=63><p>Project</p></td>
</tr>
<tr style='mso-yfti-irow:1'>
<td><p>9/3</p></td>
<td><p>Introduction to model-based inference<br>Case: synthesis of field data, remote sensing, and mechanistic models</p></td>
<td><p>Clark: Chapter 1<br>Optional: Otto and Day:<a href="http://people.bu.edu/dietze/Lectures2014/MathReview.pdf"> Math Review</a><br><a href="http://people.bu.edu/dietze/Lectures2014/Lesson01_intro.pdf">new slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:2'>
<td><p>9/5</p></td>
<td><p>Probability theory: joint, conditional, and marginal distributions<br>Case: Island Biogeography</p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2014/HM003.pdf">Hilborn and Mangel Ch 3</a> p39-62<br>Optional: Clark Appendix D<br><a href="http://people.bu.edu/dietze/Lectures2014/Lesson02_Probability.pdf">new slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:3'>
<td><p>9/8</p></td>
<td><p>Probability theory: discrete and continuous distributions<br>Case: Zero-inflated census data</p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2014/HM003.pdf">Hilborn and Mangle Ch 3</a> p62-93<br>Optional: Clark Appendix F <br><a href="http://people.bu.edu/dietze/Lectures2014/Lesson03_CommonDistributions.pdf">new slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:4'>
<td><p>9/10</p></td>
<td><p>Maximum Likelihood<br>Case: Censured mortality data</p></td>
<td><p>Chapter 3.1-3.2<br>Optional: Chapter 2<br><a href="http://people.bu.edu/dietze/Lectures2012/Lesson04_Likelihood.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:5'>
<td><p>9/12</p></td>
<td><p>Point estimation by MLE<br>Case: Survival analysis, population growth rate</p></td>
<td><p>Chapter 3.3-3.5<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson05_MLE_PointEst.pdf">old slides</a></p></td>
<td> </td>
</tr>
<tr style='mso-yfti-irow:6'>
<td><p>9/15</p></td>
<td><p>Analytically tractable MLEs<br>
Case: Bestiary of response functions</p></td>
<td><p>Chapter 3.6-3.9 <br>
Optional: Bolker <a href="http://people.biology.ufl.edu/bolker/emdbook/chap3A.pdf">Ch 3</a> <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson06_MLE3.pdf">old slides</a></p>
</td>
<td></td>
</tr>
<tr style='mso-yfti-irow:7'>
<td><p>9/17</p></td>
<td><p>Intractable MLEs and basic numerical optimization<br>
Case: Michaelis-Menton kinetics</p></td>
<td><p>Chapter 3.10-3.13 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson07_Optim.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:8'>
<td><p>9/19</p></td>
<td><p><b>EXAM 1:</b> Probability Theory, Maximum Likelihood</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:9'>
<td><p>9/22</p></td>
<td><p>Bayes Theorem<br>
Case: Detecting climate warming</p></td>
<td><p>Chapter 4.1<br>
<a href="http://people.bu.edu/dietze/Lectures2014/Ellison2004.pdf">Ellison 2004</a><br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson08_Bayes.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:10'>
<td><p>9/24</p></td>
<td><p>Point estimation using Bayes<br>
Case: Normal mean and variance</p></td>
<td><p>Chapter 4.2 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson09_Bayes2.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:11'>
<td><p>9/26</p></td>
<td><p>TBD</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:12'>
<td><p>9/29</p></td>
<td><p>Analytically-tractable Bayes: conjugacy and priors<br>
Case: Plant trait databases</p></td>
<td><p>Chapter 4.3, Appendix G <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson10_Priors.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:13'>
<td><p>10/1</p></td>
<td><p>Numerical methods for Bayes: MCMC</p></td>
<td><p>Chapter 7.1-7.2, 7.3 intro <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson11_MCMC.pdf">old slides</a></p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2012/ProjectProposal.2012.pdf"><b>Project Proposals</b></a></p></td>
</tr>
<tr style='mso-yfti-irow:14'>
<td><p>10/3</p></td>
<td><p>MCMC: Metropolis-Hastings</p></td>
<td><p>7.3.1, 7.3.2, 7.5 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson12_Metropolis.pdf"></a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:15'>
<td><p>10/6</p></td>
<td><p>MCMC: Gibbs sample</p></td>
<td><p>Chapter 7.3.3, 7.3.4 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson13_Gibbs.pdf">old slides</a><br>
<a href="http://people.bu.edu/dietze/Lectures2012/Regression_Template.odc">BUGS code</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:16'>
<td><p>10/8</p></td>
<td><p>Interval Estimation: Bayesian credible intervals</p></td>
<td><p>Chapter 5 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson14_CI.pdf">old slides</a><br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson15_CI_part2.pdf">part 2</a>
</p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:17'>
<td><p>10/10</p></td>
<td><p><b>EXAM 2:</b> Bayes</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:18'>
<td><p>10/14</p></td>
<td><p>Frequentist confidence intervals I: Likelihood profile, Fisher information</p></td>
<td><p>Chapter 5 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson16_CI_part3.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:19'>
<td><p>10/15</p></td>
<td><p>Frequentist confidence intervals II: Bootstrapping</p></td>
<td><p>Chapter 5</td>
<td></td>
</tr>
<tr style='mso-yfti-irow:20'>
<td><p>10/17</p></td>
<td><p>Model Selection: Likelihood ratio test, AIC<br>
Case: Southern Brown Frog</p></td>
<td><p>Hilborn and Mangel <a href="http://people.bu.edu/dietze/Lectures2014/HM_ch2.pdf">Chapter 2</a><br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson17_AIC.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:21'>
<td><p>10/20</p></td>
<td><p>Model Selection: DIC, predictive loss, model averaging<br>
Case: Multi-model weather forecasting</p></td>
<td><p>Chapter 6 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson18_DIC.pdf"></a></p>
</td>
<td></td>
</tr>
<tr style='mso-yfti-irow:22'>
<td><p>10/22</p></td>
<td><p>TBD</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:23'>
<td><p>10/24</p></td>
<td><p>Errors in variables, heteroskedasticity<br>
Case: Time-domain reflectometry</p></td>
<td><p>Chapter 5.4 & 7.4 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson19_regression.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:24'>
<td><p>10/27</p></td>
<td><p>Latent variables, Missing data models<br>
Case: Carbon flux towers</p></td>
<td><p>Chapter 7.6, 7.7, 8.1 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson20_regression2.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:25'>
<td><p>10/29</p></td>
<td><p>Logistic regression<br>
Case: Pollution and mortality risk</p></td>
<td><p>Chapter 8.2-8.2.3 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson21_GLM.pdf">old slides</a></p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2012/ModelDescription.2012.pdf"><b>Model Description</b></a></p></td>
</tr>
<tr style='mso-yfti-irow:26'>
<td><p>10/31</p></td>
<td><p>GLMs<br>
Case: Plot count data (Poisson regression)<br>
Case: Canopy position data (Multinomial)</p></td>
<td><p>Chapter 8.2-8.2.3</p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:27'>
<td><p>11/3</p></td>
<td><p>Mixed Models<br>
Case: RITES and species coexistence</p></td>
<td><p>Chapter 8.2.4<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson22_HB.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:28'>
<td><p>11/5</p></td>
<td><p>Multivariate Regression<br>
Case: Canopy and biomass allometries</p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2012/Lesson22.5_Classical_regression.pdf"></a><br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson22.5_Classical_regression_demo.r">R demo</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:29'>
<td><p>11/7</p></td>
<td><p>Hierarchical Bayes<br>
Case: Coho salmon</p></td>
<td><p>Chapter 8.2.5 - 8.3 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson23_HB2.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:30'>
<td><p>11/10</p></td>
<td><p>Nonlinear models<br>
Case: Photosynthetic responses to light, CO2</p></td>
<td><p>Chapter 8.4<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson24_Nonlinear.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:31'>
<td><p>11/12</p></td>
<td><p>Applications of random effects models<br>
Case: Remote sensing</p></td>
<td><p>Chapter 8.5-8.7<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson25_HB3.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:32'>
<td><p>11/14</p></td>
<td><p><b>EXAM 3</b> GLMM, HB</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:33'>
<td><p>11/17</p></td>
<td><p>TBD</p></td>
<td></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:34'>
<td><p>11/19</p></td>
<td><p>Time series: Basics and State-Space<br>
Case: Moose population fluctuations</p></td>
<td><p>Chapters 9.1, 9.2, 9.6<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson27.pdf">old slides</a></p></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2012/PrelimAnalysis.pdf"><b>Preliminary Analysis</b></a></p></td>
</tr>
<tr style='mso-yfti-irow:35'>
<td><p>11/21</p></td>
<td><p>Time series: Mark-Recapture<br>
Case: Black Noddy</span></p></td>
<td><p>Chapter 9.7, 9.8, 9.16 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson28_StateSpace.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:36'>
<td><p>11/24</p></td>
<td><p>Time series: ARMA<br>
Case: Fire in the Everglades</p></td>
<td><p>Chapter 9.3, 9.5 <br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson29_ARIMA.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:37'>
<td><p>12/1</p></td>
<td><p>Time Series: Repeated Measures<br>
Case: Soil Respiration</p></td>
<td><p>Chapter 9.10, 9.14, 9.15 <br>
<a
href="http://people.bu.edu/dietze/Lectures2012/Lesson31_RepMeas.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:38'>
<td><p>12/3</p></td>
<td><p>Spatial: point-referenced (geostatistical) data & Kriging</span><br>
Case: Mapping soil moisture</p></td>
<td><p>Chapter 10.7<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson34_PointSpatial.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:39'>
<td><p>12/5</p></td>
<td><p>Spatial: Markov Random Field<br>
Case: Superfund monitoring</p></td>
<td><p>Chapter 10.8<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson35_CAR.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:40'>
<td><p>12/8</p></td>
<td><p>Spatial: block-referenced data and misalignment<br>
Case: relating ozone & census data</p></td>
<td><p>Chapter 10.9<br>
<a href="http://people.bu.edu/dietze/Lectures2012/Lesson36_SpaceTime.pdf">old slides</a></p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:41'>
<td><p>12/10</p></td>
<td><p>Spatial: conditional autoregressive models (CAR)<br>
Case: South African biodiversity</p></td>
<td><p>Chapter 10.10</p></td>
<td></td>
</tr>
<tr style='mso-yfti-irow:42;mso-yfti-lastrow:yes'>
<td><p>TBD</p></td>
<td><p><b>EXAM 4</b></p></td>
<td></td>
<td><p><a href="http://people.bu.edu/dietze/Lectures2012/FinalProject.pdf"><b>FINAL PROJECT</b></a></p></td>
</tr>
</table>
<p><b><span style='font-size:14.0pt'><o:p> </o:p></span></b></p>
<p><b><span style='font-size:14.0pt'>Lab Syllabus<o:p></o:p></span></b></p>
<table border=1 cellspacing=0 cellpadding=0>
<tr>
<td width=32> <p>Lab</p> </td>
<td width=45><p>Week</p></td>
<td width=283><p>Topics</p></td>
<td width=70><p>Software</p></td>
</tr>
<tr style='mso-yfti-irow:1'>
<td width=32><p>1</p></td>
<td width=45><p>9/8</p></td>
<td width=283><p>Introduction to R</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:2'>
<td width=32><p>2</p></td>
<td width=45><p>9/15</p>
</td><td width=283><p>Probability distributions and sampling</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:3'>
<td width=32><p>3</p></td>
<td width=45><p>9/22</p></td>
<td width=283><p>Fire return intervals: Maximum likelihood basics</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:4'>
<td width=32><p>4</p></td>
<td width=45><p>9/29</p></td>
<td width=283><p>Ecosystem responses to CO2: ML numerical optimization</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:5'>
<td width=32><p>5</p></td>
<td width=45><p>10/6</p></td>
<td width=283><p>Forest stand characteristics: Intro to BUGS</p></td>
<td width=70><p>BUGS</p></td>
</tr>
<tr style='mso-yfti-irow:6'>
<td width=32><p>6</p></td>
<td width=45><p><b>10/14*</b></p></td>
<td width=283><p>Regression: Gibbs sampler</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:7'>
<td width=32><p>7</p></td>
<td width=45><p>10/20</p></td>
<td width=283><p>Nonlinear plant growth: Metropolis Algorithm</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:8'>
<td width=32><p>8</p></td>
<td width=45><p>10/27</p></td>
<td width=283><p>CO2 revisited: Interval estimation and model selection</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:9'>
<td width=32><p>9</p></td>
<td width=45><p>11/3</p></td>
<td width=283><p>Understory Regeneration: Random effects</p></td>
<td width=70><p>Both</p></td>
</tr>
<tr style='mso-yfti-irow:10'>
<td width=32><p>10</p></td>
<td width=45><p>11/10</p></td>
<td width=283><p>Mosquito abundance: Hierarchical modeling</p></td>
<td width=70><p>WinBUGS</p></td>
</tr>
<tr style='mso-yfti-irow:11'>
<td width=32><p>11</p></td>
<td width=45><p>11/17</p></td>
<td width=283><p>Moose population fluctuations: State-space time series</p></td>
<td width=70><p>WinBUGS</p></td>
</tr>
<tr style='mso-yfti-irow:12'>
<td width=32><p>12</p></td>
<td width=45><p>11/24</p></td>
<td width=283><p>Peer Assessment of projects</p></td>
<td width=70><p><o:p> </o:p></p></td>
</tr>
<tr style='mso-yfti-irow:13'>
<td width=32><p>13</p>
</td><td width=45><p>12/1</p></td>
<td width=283><p>Ozone: Space/time exploratory data analysis</p></td>
<td width=70><p>R</p></td>
</tr>
<tr style='mso-yfti-irow:14;mso-yfti-lastrow:yes'>
<td width=32><p>14</p></td>
<td width=45><p>12/8</p></td>
<td width=283><p>South African biodiversity: Spatial CAR and Kriging</p></td>
<td width=70><p>WinBUGS</p></td>
</tr>
</table>
<p><b><o:p> </o:p></b></p>
<p style='margin-top:2.9pt;margin-right:0in;margin-bottom:2.9pt;
margin-left:0in;line-height:12.0pt;mso-line-height-rule:exactly;mso-layout-grid-align:
none;text-autospace:none'><b><span style='font-size:14.0pt;
"American Typewriter";color:black'>Academic Code<o:p></o:p></span></b></p>
<p style='margin-top:2.9pt;margin-right:0in;margin-bottom:2.9pt;
margin-left:0in;line-height:12.0pt;mso-line-height-rule:exactly;mso-layout-grid-align:
none;text-autospace:none'><span style='"American Typewriter Light";
color:black'>It is your responsibility to know and understand the provisions of
the CAS Academic Conduct Code. Copies are available in CAS 105. Suspected cases
of academic misconduct will be referred to the Dean’s Office.<span
style="mso-spacerun:yes"> </span>See </span><a
href="http://www.bu.edu/academics/resources/academic-conduct-code"><span
style='Helvetica'>http://www.bu.edu/academics/resources/academic-conduct-code</span></a><span
style='color:#003E9F'> </span><span
style='"American Typewriter Light";color:black'>for
conduct information for undergraduates and </span><a
href="http://www.bu.edu/cas/students/graduate/forms-policies-procedures/academic-discipline-procedures/"><span
style='"American Typewriter Light"'>http://www.bu.edu/cas/students/graduate/forms-policies-procedures/academic-discipline-procedures/</span></a><span
style='"American Typewriter Light";color:black'> for
graduate student conduct requirements.<o:p></o:p></span></p>
<p><b><o:p> </o:p></b></p>
<p><b><o:p> </o:p></b></p>
<p><b><span style='font-size:14.0pt'>Additional Resources<o:p></o:p></span></b></p>
<p><b><span style='font-size:14.0pt'><o:p> </o:p></span></b></p>
<p><b>Books<o:p></o:p></b></p>
<p style='margin-left:0in;text-indent:0in;'><i
style='mso-bidi-font-style:normal'><o:p> </o:p></i></p>
<p style='margin-left:0in;text-indent:0in;'><i
style='mso-bidi-font-style:normal'>Ecological Perspectives<o:p></o:p></i></p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Hilborn</span>, R., M. Mangel</span>. 1997. The
Ecological Detective. Princeton University Press.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Bolker</span>, B. 2008. <a
href="http://people.biology.ufl.edu/bolker/emdbook/index.html">Ecological
Models and Data and R</a></p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Clark,
J.S. & A. E. Gelfand</span>. 2006. Hierarchical <span
class=SpellE>Modelling</span> for the Environmental Sciences: Statistical
Methods and Applications. Oxford University Press.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>McCarthy,
M. 2007. Bayesian methods for Ecology. Cambridge University Press.</p>
<p style='margin-left:0in;text-indent:0in;'><o:p> </o:p></p>
<p style='margin-left:0in;text-indent:0in;'><o:p> </o:p></p>
<p style='margin-left:0in;text-indent:0in;'><span
class=SpellE><i style='mso-bidi-font-style:normal'>WinBUGS</i></span><i
style='mso-bidi-font-style:normal'> examples<o:p></o:p></i></p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Congdon</span>, P. 2001. Bayesian Statistical Modelling</span>.
John Wiley & Sons.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Congdon</span>, P. 2003. Applied Bayesian Modelling</span>.
John Wiley & Sons.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Congdon</span>, P. 2005. Bayesian Models for Categorical Data.
John Wiley & Sons.</p>
<p style='margin-left:0in;text-indent:0in;'><o:p> </o:p></p>
<p style='margin-left:0in;text-indent:0in;'><i
style='mso-bidi-font-style:normal'>Intro Bayesian & Hierarchical Bayes<o:p></o:p></i></p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Bolstad</span>, W. M. 2007. Introduction to Bayesian Statistics,
2nd Edition. John Wiley & Sons, Inc., Hoboken, NJ.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Gelman</span>, A., J.B. Carlin, H.S. Stern, D.B. Rubin. 2003.
Bayesian Data Analysis, 2nd Edition. Chapman & Hall/CRC Press.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Gelman</span>, A. & J. Hill. 2007. Data Analysis Using
Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Silvia,
D.S. & Skilling J. 2006. Data Analysis: A Bayesian Tutorial, 2nd Edition.
Oxford University Press, Oxford.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Winkler,
R.L. 2003. An Introduction to Bayesian Inference and Decision. Probabilistic
Publishing, Gainesville, FL.</p>
<p style='margin-left:0in;text-indent:0in;'><o:p> </o:p></p>
<p style='margin-left:0in;text-indent:0in;'><i
style='mso-bidi-font-style:normal'>Statistical books<o:p></o:p></i></p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Albert,
J. 2008. Bayesian Computation with R. Springer. 270 pages.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Carlin,
B.P., T.A. Louis. 2000. Bayes and Empirical Bayes Methods for Data Analysis. <span
class=SpellE>Chapmall</span> & Hall/CRC.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Gamerman</span>, D., H. F. Lopes 2006. Markov Chain Monte Carlo:
Stochastic Simulation for Bayesian Inference, 2nd Edition. Chapman &
Hall/CRC.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><span
class=SpellE>Gilks</span>, W.R., S. Richardson, D.J. Spiegelhalter</span>.
1996. Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'>Marin,
J.-M., C. P. Robert. 2007. Bayesian Core: A Practical Approach to Computational
Bayesian Statistics. Springer, New York.</p>
<p style='margin-left:0in;text-indent:0in;mso-list:l0 level2 lfo1'><o:p> </o:p></p>
<p><b>General review articles<o:p></o:p></b></p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Special
issue in Ecology. 2003. Ecological Uncertainty and Forecasting. Vol. 84(6):
1349-1414.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Special
issue in Ecological Applications. 1996. Bayesian Inference. Vol. 6(4): 1034+.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Special
issue in Ecological Applications. 2006. Deepening Ecological Insights Using
Contemporary Statistics. Vol. 16(1): 3-124.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Forum
articles in Ecological Applications. 2008. Forum-Hierarchical Statistical
Models in Ecology. Vol. 19(3): 551-596.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Ellison
(2004) Bayesian inference in ecology. Ecology Letters 7:509-520.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Clark
(2005) Why Environmental scientists are becoming Bayesians. Ecology Letters
8:2-14.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Clark
& Gelfand</span> (2006) A future for models and data in
environmental sciences. Trends in Ecology & Evolution 21:375-380.</p>
<p style='margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span
style='font-size:9.0pt;
'>-<span
style='font:7.0pt '> </span></span></span><![endif]>Ogle
& Barber (2008) Bayesian data-model integration in plant physiological and
ecosystem ecology. Progress in Botany Vol. 69:281-311.</p>
<p><o:p> </o:p></p>
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