Intermediate course focused on statistical modeling in a Bayesian framework, with applications in the biological and environmental sciences. Topics will include probability, causal inference, and generalized linear models. Classroom activities will be a mix of lecture, discussion, and problem sets. We will take a hands-on, computational approach (R, Stan) to gain intuition so that students can design their own inferential models. Outside of class, students will watch pre-recorded lectures and complete readings. Note: Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply.
3 units · Letter or Credit/No Credit
Intermediate course focused on statistical modeling in a Bayesian framework, with applications in the biological and environmental sciences. Topics will include probability, causal inference, and generalized linear models. Classroom activities will be a mix of lecture, discussion, and problem sets. We will take a hands-on, computational approach (R, Stan) to gain intuition so that students can design their own inferential models. Outside of class, students will watch pre-recorded lectures and complete readings. Note: Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply.