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COVID-19 Research Resources
A curated list of research resources around guidelines, policies, and procedures related to COVID-1, drawn from Harvard University, affiliated academic healthcare centers, and government funding agencies

COVID-19 Research Resources
A curated list of research resources around guidelines, policies, and procedures related to COVID-1, drawn from Harvard University, affiliated academic healthcare centers, and government funding agencies

Calendar

Biostatistics short course: Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling

October 1 – October 2, 2018
Kresge G3, Harvard T.H. Chan School of Public Health map

Biostatistics short course: Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling-10/01/2018

Session Dates: October 1-2, 2018
This course is intended for statisticians who are interested in learning the foundations of Bayesian inference and prediction in the context of regression and multilevel models. The course will be helpful for statisticians who wish to learn about the use of R as an environment for Bayesian computations. It is assumed that the participant has some basic familiarity with the R system.

The basic tenets of Bayesian thinking are introduced, including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking. There will be a focus on Bayesian regression for continuous and categorical response data.  The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior.

Course Instructor:
Jim Albert, PhD
Distinguished University Professor, Department of Mathematics and Statistics
Bowling Green State University

Course Agenda

Presentation Slides

Day 1 [PDF]

Day 2 [PDF]

Day 2 Case study example in R [PDF]

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