MATH 611A series of courses in Probability
- Level
- Graduate
- Faculty
- Mathematics
- Last listed
- 1967–68
No longer offered. It’s in Waterloo’s scanned calendars from 1965–66 to 1967–68 (before that, 1963–64 to 1964–65 used the number for Advanced Probability); the 1995–96 to 2023–24 calendars are on ucalendar.
(a) Introduction to Random Variables. Probability, random variables, distribution functions, expectation, discrete transform methods. 1 term. (b) Theory of Probability. Measures on a sample space, types of convergence, limit theorems, laws of large numbers. 1 term. ({c) Stochastic Processes I. Discrete space — discrete time processes. Discrete space — continuous time processes. Applications in Physics, Biology, Queuing Theory, etc. 1 term. (d) Stochastic Processes II. Mathematical foundations of stochastic processes, continuous space processes. Time-series analysis. {e) Special Topics in Probability. 1 term.
As printed in the 1965–66 to 1967–68 calendars, read by OCR; the scanned page is the original.
Check it elsewhere
In the scanned calendars
How it read in each calendar from 1963–64 to 1967–68, newest first. The text is OCR of the scanned pages; years whose wording matches are shown once, from the clearest scan. Each year links to its page on uwloo and to that page of the scanned PDF.
1965–66 to 1967–68 3 calendars
(a) Introduction to Random Variables. Probability, random variables, distribution functions, expectation, discrete transform methods. 1 term.
(b) Theory of Probability. Measures on a sample space, types of convergence, limit theorems, laws of large numbers.
1 term.
({c) Stochastic Processes I. Discrete space — discrete time processes. Discrete space — continuous time processes. Applications in Physics, Biology, Queuing Theory, etc.
1 term.
(d) Stochastic Processes II. Mathematical foundations of stochastic processes, continuous space processes. Time-series analysis.
{e) Special Topics in Probability. 1 term.
1963–64 to 1964–65 2 calendars
Another title: Advanced Probability, probably a different course under the same number (or a misread scan).
Review of classical probability theory, including occupancy problems, the binomial, Poisson, and nomal distributions, applications in physics. Recurrent events. Markov chains, stochastic processes, including the ruin problem and diffusion processes; application to information theory, radar, queuing problems, etc.