Bayesian Modeling for Biomedical Research & Public Health

CT Score

1500

#0 of 0 in SPH BS

Difficulty

—/10

Workload

—h/wk

Median grade

—

from grade distribution

Would take again

—

n = 0 verified

Grade distribution

No distribution published for this course.

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

Professor ratings →

Sections

No section data for this term yet.

In the tree

At least one course of statistics to cover principles of probability a nd statistical inference, linear and logistic regression. Knowledge of R. - The purpose of this course is to present Bayesian modeling techniques in a variety of data analysis applications, including both hypothesis and data driven modeling. The course will start with an overview of Bayesian principles through simple statistical models that will be used to introduce the concept of marginal and conditional independence, graphical modeling and stochastic computations. The course will proceed with the description of advanced Bayesian methods for estimation of odds and risk in observational studies, multiple regression modeling, loglinear and logistic regression, hierarchical models, and latent class modeling including hidden Markov models and application to model-based clustering. Applications from genetics, genomics, and observational studies will be included. These topics will be taught using real examples, class discussion and critical reading. Students will be asked to analyze real data sets in their homework and final paper→SPH BS 849→unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups

0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.