Course Syllabus

Applied Bayesian Statistics (MEES608R)

Spring 2017



Dong Liang(CBL;; 410-326-7452)

Lectures: Monday and Wednesday 11:00-12:00 pm IVN (TBD) Office hours: Wednesday 1:00 – 2:30 pm

Course Objective:

This course will introduce mixed effect modelling from a Bayesian perspective. Mixed model is an unifying framework for analysing continuous, count, presence / absence and zero inflated data from environmental applications. We will explore the selection, interpretation and reporting of Bayesian mixed modelling results. The statistical programming language R and packages R-INLA, JAGS will be used in the labs and projects.

Reference Textbooks and Website:

  • Cowles, M.K., Applied Bayesian Statistics, Springer
  • Zurr, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., and Smith, G.M., Mixed Effects Models and Extensions in Ecology with R, Springer
  • Rue, H. et al.,

Grading and philosophy for the class:

Grades will be based on a Pass and Fail system. Students are encouraged to carry out an independent project. They also have the option of leading the discussion of a peer reviewed paper.


There will be several lab sessions. Students are encouraged to apply the mixed effect modelling concepts and R tools to analyse published data sets.


There will be no exam in this class.


Discussion papers can be selected by students based on research interests. Please first consent the instructor. Discussion items will include understanding what the authors did, if or why a Bayesian approach was a good option, whether their choice of methods was appropriate, and whether you agree with the authors’ interpretation.


Students are encouraged to carry out individual project involving application of mixed effect models to problems of their own choosing by analyzing a real data set from their research. This might involve description of the research question and dataset, selecting an appropriate model, determining appropriate values for prior parameters, fitting the model using JAGS or R-INLA, checking convergence, and reporting and interpreting the results.

Projects will be carried out in three phases. Please consult with the instructor at least once while you are working on each phase.

  1. Project proposal is a short 2 paragraph description of what you plan to do, including question(s) to be addressed, dataset to be used, and methods to be applied.
  2. Project interim report is a 5 pager, indicating that your project is on track. All computing should be done at this time. The report will include results obtained thus far and a brief summary (hand-written is OK) of what they mean and what remains to be done.
  3. Project presentation (presentation materials must be posted or submitted). Projects must be finalized in a form that can be shared with the entire class, such as posting a document on the course web page, preparing a poster, and giving an oral presentation with overheads, slides, or computer images. Posters and oral presentations will be given in class during the final week of classes.

Distribution of class materials:

For the first several class periods, we will email reminders to get the info for class and where the info will be located. Please bookmark the Moodle site ( in your web browser so that you can rapidly get there.

We will be using the distance learning tool, Moodle for storing and disseminating class information – class notes, computer code and output, assigned readings, and even discussion threads if you wish. Each student will be given a personal login and password to access the site. Materials for the next class will be posted no later than 12 hours before the beginning of the class. You are strongly encouraged to download and bring the R code and output to each class as these are critical components of the lectures and may be hard to follow without having these in front of you.

Spring SEMESTER 2017 calendar

First Day of Classes                   January 25 (Wednesday)

Spring Break                             March 19-26 (Sunday-Sunday)

Project Proposal                        April 20 (Friday)

Project Interim Report             May 2 (Wednesday)

Last Day of Classes                    May 11 (Thursday)

Project Presentation                  May 4,11 (Thursdays)


Tentative Course Calendar




Review of linear regression

Zurr Appendix A

Linear mixed model and pseudo-replication

Zurr Ch 5

Linear mixed model and spatial correlation

Zurr Ch 7

Generalized linear model (GLM)

Zurr Ch 9

GLM and presence/absence data

Zurr Ch 10

GLM and zero inflated data

Zurr Ch 11

Generalized linear mixed model (GLMM)

Zurr Ch 16

GLMM and spatial data

Zurr Ch 21

Generalized additive mixed model (GAMM) and count data

Zurr Ch 21

GAMM and presence only data








Class presentations


Class presentations