Essentials of Probability and Statistical Inference I: Probability
- 1st term
- 4 credits
- Academic Year:
- 2020 - 2021
- Instruction Method:
- Class Times:
- M W, 3:30 - 4:50pm
Working knowledge of linear algebra, including the ability to invert a matrix; full year college level calculus, plus current working knowledge of it, meaning you can quickly do integration and differentiation of standard functions, which are needed for homework and exam questions.
Introduces students to the theory of probability and the formal language of uncertainty. Includes the basic concepts of probability; random variables and their distributions; joint, marginal and conditional distributions; independence; distributions of functions of random variables; expectations; moment generating functions; probability and expectation inequalities; convergence concepts and limit theorems; order statistics. Emphasizes rigorous analysis (including proofs), as well as interpretation of results and simulation for illustration.
- Learning Objectives:
- Discuss the probabilistic foundation of modern statistics
- Solve basic probability problems
- Methods of Assessment:
Method of student evaluation based on 6 problem sets (60%) and a final exam (40%).
- Instructor Consent:
Consent required for some students
- Consent Note:
Course intended for Biostatistics ScM and MHS candidates only; consent needed for anyone who is not a Biostatistics PhD, ScM, or MHS student.
- For consent, contact: