| Call Number | 11126 |
|---|---|
| Day & Time Location |
MW 8:40am-9:55am 633 Seeley W. Mudd Building |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Kyle Bishop |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Course is aimed at senior undergraduate and graduate students. Introduces fundamental concepts of Bayesian data analysis as applied to chemical engineering problems. Covers basic elements of probability theory, parameter estimation, model selection, and experimental design. Advanced topics such as nonparametric estimation and Markov chain Monte Carlo (MEME) techniques are introduced. Example problems and case studies drawn from chemical engineering practice are used to highlight the practical relevance of the material. Theory reduced to practice through programming in Mathematica. Course grade based on midterm and final exams, biweekly homework assignments, and final team project. |
| Web Site | Vergil |
| Department | Chemical Engineering |
| Enrollment | 32 students (60 max) as of 10:06AM Thursday, November 21, 2024 |
| Subject | Chemical Engineering |
| Number | E4670 |
| Section | 001 |
| Division | School of Engineering and Applied Science: Graduate |
| Open To | Barnard College, Engineering:Undergraduate, Engineering:Graduate, GSAS |
| Section key | 20243CHEN4670E001 |