JEFF GILL: Teaching Page
JEFF GILL: Teaching Page
Courses and Workshops
•Harvard University: Government 61, Research Practice in Quantitative Methods time: Wednesday 2-4, location: TBD.
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Syllabus .
•Harvard University: Government 2003, Bayesian Hierarchical Models time: Thursday 10-12, location: TBD.
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Syllabus .
•American University: Statistics 618/SPA 696 (Every FALL): Bayesian Statistics for Social and Biomedical Sciences Thursday, 2:30-5:20PM, location: TBD.
◦Course Description: Principles and applications of modern statistical decision theory, with a special focus on Bayesian modeling, data analysis, inference, and optimal decision making. Prior and posterior; comparison of Bayesian and frequentist approaches, including minimax decision making and elementary game theory. Bayesian estimation, hypothesis testing, credible sets, and Bayesian prediction. Introduction to Bayesian computing software and applications to diverse fields. Grading: A-F only. Prerequisite: STAT-514 or permission of instructor.
◦Eligibility: All graduate and professional students meeting the prerequisites (below) are eligible. Previous attendees have included residents, medical students, fellows, Arts & Sciences Ph.D. students, Brown School Ph.D. students, and practicing researchers in the medical school.
◦Prerequisite Details: This course assumes only 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 either scalar or matrix form. Very basic knowledge of matrix algebra and calculus is convenient but not required. The course will make extensive use of the R statistical language.
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Beginning R Workshop (do not print slides!).
Kerwin Hall 311, Monday 3-4:30 for 9/25, 10/9, 10/23, 11/6, 11/13, 11/20, 11/27.
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Applied Bayesian Data Analysis,
Statistical Horizons Workshop, November 3-4,
Instructor Profile.
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Bayesian Hierarchical Modeling for the Social Sciences.
Workshops on Social Science Research (WSSR), Concordia University, Canada. Dates: June 7, 2017 – June 8,
2017, 9 am – 4:30 pm.
Desription: This course covers the theoretical and applied
foundations of Bayesian statistical analysis with an emphasis on computational
tools for Bayesian hierarchical models. We will discuss model checking, model
assessment, and model comparison. The course will cover Bayesian stochastic
simulation (Markov chain Monte Carlo) in depth with an orientation towards
deriving important properties of the Gibbs sampler and the Metropolis Hastings
algorithm. Extensions and hybrids will be discussed. We will then use Markov
chain Monte Carlo tools to fit linear and nonlinear specifications with
multiple levels, longitudinal features, and non-normal distributional
assumptions. Lectures will include theoretical discussions of modeling and
estimation as well as practical guidance for fitting complex multilevel models
with R and JAGS software. Applications will be drawn from political science,
sociology, epidemiology, economic policy, and public administration.