6.041/6.431 Probabilistic Systems Analysis and Applied Probability
6.041/6.431 Probabilistic Systems Analysis and Applied Probability (Fall 2010, MIT OCW). Instructor: Professor John Tsitsiklis. Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. The aim of this course is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition. (from ocw.mit.edu)
| Lecture 06 - Discrete Random Variable Examples; Joint PMFs |
In this lecture, the professor discusses conditional PMF, geometric PMF, total expectation theorem, and joint PMF of two random variables.
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