PROBABILITY AND RANDOM PROCESSES
Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such as E C E 331 , STAT/MATH 431 , or STAT 311 ] strongly encouraged.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2024 Learning Outcomes: 1. Compute probabilities and expectations using probability mass functions and densities together with the laws of total probability and substitution, along with the property of independence when applicable Audience: Graduate 2. Work with Gaussian random vectors, joint densities, and characteristic functions Audience: Graduate 3. Determine in what sense(s) a sequence of random variables converges Audience: Graduate 4. Perform calculations using properties of the Poisson process and the Wiener process Audience: Graduate Learning Outcomes: 1. Compute probabilities and expectations using probability mass functions and densities together with the laws of total probability and substitution, along with the property of independence when applicable Audience: Graduate 2. Work with Gaussian random vectors, joint densities, and characteristic functions Audience: Graduate 3. Determine in what sense(s) a sequence of random variables converges Audience: Graduate 4. Perform calculations using properties of the Poisson process and the Wiener process Audience: Graduate
Sources
- Course catalogue ↗2026-2027