This course provides a foundation in the theory and applications of probability and stochastic processes and an understanding of the mathematical techniques relating to random processes in the areas of signal processing, detection, estimation, and communication. Topics include the axioms of probability, random variables, and distribution functions; functions and sequences of random variables; stochastic processes; and representations of random processes. Prerequisite s : A working knowledge of multi-variable calculus, Fourier transforms, and linear systems theory. The overall goal of this course is that you will be able to master the fundamentals of probability and stochastic processes mentioned in the course description above.
Stochastic Processes (Advanced Probability II), 36-754
Homework – ESE
The course is on Canvas. Homework 1 Homework 2 Homework 3 Homework 4 Homework 5. Zoom link available in Canvas Classes will be live and recorded. Recording will be available for viewing in Canvas.
625.721—Probability and Stochastic Process I Course Homepage
Prereq: Graduate status and MA Unnikrishna Pillai. Posted on December 12, Final exam room assignments.
This rigorous course in probability covers probability space, random variables, functions of random variables, independence and conditional probabilities, moments, joint distributions, multivariate random variables, conditional expectation and variance, distributions with random parameters, posterior distributions, probability generating function, moment generating function, characteristic function, random sum, types of convergence and relation between convergence concepts, law of large numbers and central limit theorem i. This course is proof oriented. The primary purpose of this course is to lay the foundation for the second course, EN. Note that, in contrast to EN. The goal of this course is to develop a deeper understanding of various probability topics using a rigorous, proof-based, non-measure theoretic approach.