Welcome to Stats 115 - Introduction to Bayesian Data Analysis.
For anyone interested, in an earlier version of this course you can take a look at the course website from Summer II 2021. The older link may not be useful for students currently enrolled in the course. It is shared mainly for instructors and learners elsewhere.
Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Includes linear, logistic, and Poisson regression. Analyses are performed using Stan with rstan package in R.
Prerequisite: STATS 120C
Recommended STATS 110
Concurrent with STATS 203
For anyone interested, in an earlier version of this course you can take a look at the course website from Summer II 2021. The older link may not be useful for students currently enrolled in the course. It is shared mainly for instructors and learners elsewhere.
Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Includes linear, logistic, and Poisson regression. Analyses are performed using Stan with rstan package in R.
Prerequisite: STATS 120C
Recommended STATS 110
Concurrent with STATS 203
Course Goals
By the end of this course you will be able to:
By the end of this course you will be able to:
- distinguish frequentist and Bayesian approaches to data analysis;
- choose appropriate prior distributions for a Bayesian model;
- use Stan to fit Bayesian models;
- interpret Bayesian model results.
Typical Week workflow
for in-person instruction.
-
Monday 9:30 am
Previous week's homework is due.
-
Monday 9:30 -10:50 am
Attend lecture
-
Wednesday 9:30 -10:50 am
Attend lecture
-
Wednesday 11:00 -11:50 am
Attend discussion
-
Thursday 5 - 6 pm
Prof. Dogucu holds office hours.
-
Thursday 11:59 pm
Weekly quizzes are due.
-
Friday 3 - 4 pm
Prof. Dogucu holds office hours.
Important Dates
Midterm Exam Feb 9, 09:30 - 10:50 am
Final Exam due Mar 16, 8:00 - 10:00 am