Probability and Random Processes
Graduate course covering advanced probability, random vectors, linear filtering, stationarity, PSDs, estimation, convergence, Markov chains, Poisson, and Wiener processes.
Summary
Past student reviews · John Gubner
The workload was substantial, requiring deep understanding of fundamentals to succeed. Reviewers characterized Dr. Gubner as a tough grader who demanded precise answers on tests and was known for failing students who did not meet his rigorous standards.
What you’ll be able to do
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
Work with Gaussian random vectors, joint densities, and characteristic functions
Determine in what sense(s) a sequence of random variables converges
Perform calculations using properties of the Poisson process and the Wiener process
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
JOHN GUBNER is recorded teaching in Spring 2009, Spring 2011, Fall 2012, Fall 2013, Fall 2014, Fall 2015, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Past student reviews · John Gubner
Students described the course as a graduate-level probability class that was difficult to digest due to its heavy theoretical focus. Reviewers noted that Dr. Gubner taught with an excessively mathematical approach, leaving little room for physical examples or intuition.
Recent recorded grades — Fall 2022: 3.50 GPA, 70.6% A/AB (n=17 letter grades); Fall 2023: 4.00 GPA, 100.0% A/AB (n=7 letter grades); Fall 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades).
difficulty & workload
Past student reviews · John Gubner
The workload was substantial, requiring deep understanding of fundamentals to succeed. Reviewers characterized Dr. Gubner as a tough grader who demanded precise answers on tests and was known for failing students who did not meet his rigorous standards.
Past student reviews · John Gubner
While Dr. Gubner was patient and willing to answer questions, students found his lectures dry and impossible to follow. Some reviewers felt he gave tricky exam questions without explaining them in class, forcing students to rely heavily on textbooks.
Topics
- Random vectors and linear filtering
- Stationarity and power spectral densities
- Estimation and convergence
- Markov chains, Poisson process, and Wiener process
Skills
- Compute probabilities and expectations using PMFs, densities, and laws of total probability/substitution
- Work with Gaussian random vectors, joint densities, and characteristic functions
- Determine senses of convergence for sequences of random variables
- Perform calculations using properties of Poisson and Wiener processes
Grades
Latest available · Fall 2024— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2024
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "8443a4734f50c4820ad8a4a7d6763f9f5d8c89e677176290ee4c24de5be36687",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}