Probability and Bayesian Statistics (MAT4221.01)

Kathryn Montovan

This course will provide a theoretically rigorous introduction to Bayesian Statistics. We will begin with concepts from probability, build to the Bayesian theory, and apply what we learn to analyze common types of data using the computer program R. The Bayesian approach to data analysis will be compared with the more commonly-taught Frequentist approaches and students. Students will be expected to participate fully in class discussions, understand and explain the theory related to the probability and Bayesian statistics, and demonstrate that they can use Bayesian statistics to analyze data in R. The course will culminate in individual projects where students will be expected to design their own project, collect or acquire relevant data, apply relevant skills, and communicate the results orally and in writing.

Note: If you are accessing the course remotely you will need a tablet that you can write on so that you can participate in group work. Please talk to the instructor if this is a concern.

Learning Outcomes:
In this course, you will:
• Learn and apply probability theory to rigorously understand Bayesian statistics for several different types of analysis
• Present new ideas and lead discussions about the mathematics we are learning
• Use R to apply concepts to data and problems
• Compare Bayesian statistical methods to frequentist methods
• Develop a question, acquire relevant data, use R and Bayesian statistics to analyze the data and answer your question, and present/communicate your results.

Delivery Method: Remotely accessible
Prerequisites: Prerequisites: Creation of Statistics (or a similar statistics course taught in R), mathematical maturity demonstrated through advanced mathematics and/or computer science course work, and permission of instructor.
Course Level: 4000-level
Credits: 4
T/F 8:30AM - 10:20AM (Full-term)
Maximum Enrollment: 12
Course Frequency: Every 2-3 years

Categories: All courses , Mathematics , Remotely Accessible
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