Stochastic Modeling Techniques
Recorded 2026-10-14 Spring 2027
Stochastic Modeling Techniques
Stochastic Modeling Techniques covers modeling systems with uncertainty, emphasizing application over derivation, using case studies from logistics, production, and service industries.
offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed
Summary
1 / 3
Recent recorded grades — Spring 2024: 3.80 GPA, 90.0% A/AB (n=10 letter grades); Spring 2025: 3.50 GPA, 69.2% A/AB (n=13 letter grades); Spring 2026: 3.72 GPA, 88.9% A/AB (n=9 letter grades).
What you’ll be able to do
Identify and interpret basic concepts of probability, including random variables, and conditional probability, expectation, and variance
Analyze probability models such as the Markov chains, the exponential distribution, Poisson processes, and queuing models
Identify, formulate, and develop solution techniques for problems that can be modeled by stochastic models in various domains (engineering, computer science, supply chains, healthcare systems, operations research)
Recognize how and when to apply each type of probability model
Formulate mathematical formulations and apply proof techniques
Source: UW Guide
Grade history
↗ average GPA
375375 letter grades
A40.0%40.0%
AB26.1%26.1%
B19.5%19.5%
BC8.8%8.8%
C4.8%4.8%
D0.8%0.8%
F0.0%0.0%
All recorded terms · compare terms & instructors
Prerequisites
Course map(STAT 311,STAT/MATH 309, 409, or 431) and (MATH 320, 340, 341, 345, or 375), graduate/professional standing, or member of Engineering Guest Students
- take one
- take one
- graduate/professional standing
- member of Engineering Guest Students
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- All of
- Any of
- graduate/professional standing
- member of Engineering Guest Students
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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 |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 25 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Recent recorded grades — Spring 2024: 3.80 GPA, 90.0% A/AB (n=10 letter grades); Spring 2025: 3.50 GPA, 69.2% A/AB (n=13 letter grades); Spring 2026: 3.72 GPA, 88.9% A/AB (n=9 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Uncertainty modeling
- Probability models including Markov chains and queuing
- Applications in logistics, production, and services
Skills
- Modeling systems with uncertainty
- Interpreting basic probability concepts
- Analyzing specific probability models
- Developing solution techniques for stochastic problems
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
average GPA
A / AB grades
letter grades
Instructor
Grade distribution · % of letter grades
A55.6%55.6%
AB33.3%33.3%
B11.1%11.1%
BC0.0%0.0%
C0.0%0.0%
D0.0%0.0%
F0.0%0.0%
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
UW–Madison
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
Raw records
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"term_id": "1274",
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"title": "Stochastic Modeling Techniques",
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Student reviews
Original comments behind the course and instructor summaries.
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Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesDataset & model
View the dataset on Hugging Face Recorded 2026-10-14
Model outputs & technical records
Qwen/Qwen3.8-27B
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": "b1745483456de712d0f7dddfb4a5d5cb7a984196736b9a3299b829b609a3732b",
"requirements_status": "needs_review",
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"observed_at": "2026-10-14 17:50:09.728220+00:00"
}