Harvard University: Government 2003, Bayesian Hierarchical Models

Semester: 

Spring

Offered: 

2018

Course Description: 
This course covers Bayesian statistical model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modeling of variation between defined groups. The course begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured. The focus on the course will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and bugs environments. 

Learning Outcomes: 
At the conclusion of this course participants will: be able to specify and estimate Bayesian multilevel (hierarchical) models with linear and nonlinear outcomes, treat missing data in a principled and correct manner using multiple imputation, gain facility in the R and bugs statistical languages, know how to compute the appropriate sample size and power calculations for Bayesian models, gain exposure to Bayesian approaches including MCMC computation, and be able to assess model reliability and fit in complex models. 

Prerequisite Details: 
This course assumes a knowledge of basic statistics as taught in a first year graduate sequence. Topices should include: probability, cross-tabulation, basic statistical summaries, and linear regression in matrix form. and knowledge of R. Exposure to basic matrix algebra and calculus is helpful. 

Course Grade: 
The final grade will be based on two components: weekly attendance and participation (20%) and exercises (80%). Exercises are due one week after assignment on the syllabus. 

Office Hours: 
Wednesday 10-12, in the CGIS South building, Room S407.

Incompletes: 
Due to the scheduled nature of the course, no incompletes will be given.

Teaching Fellow: 
Jonathan Homola. Office Hours: TBD.

Required Text: 
Gelman and Hill, "Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press 2007). Other readings will be papers will made available at jstor.org or distributed by the instructor on this syllabus/webpage. Readings should be completed before class listed on the syllabus.

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