Fall 2026

Introduction to Stochastic Processes

Introduction to stochastic processes covering Markov chains, point processes, and renewal theory with applications to queueing and branching models.

offering recorded3 credits
Recorded instructors · Fall 2026 Timo Seppalainen4.4/5Benedek Valko3.9/5

Summary

1 / 6

Seppalainen assigns challenging homework that is harder than exams, while Valko uses a grading scheme based on multiple weighted components rather than heavy testing.

Grade history

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average GPA
letter grades
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AB
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(STAT/​MATH 431, 309,STAT 311, or MATH 531) and (MATH 320, 340, 341, 345, 375, 421, or 531), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

ISYE/MATH/OTM/STAT 632 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026

Timo Seppalainen

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 5.00/5 from 22 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 5.0/5 raw quality · 3.3/5 difficulty · 6 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Timo Seppalainen receives universal praise for clear, engaging lectures and making complex probability concepts accessible. Reviewers highlight his professionalism, helpfulness, and deep care for student success. While some note challenging homework, the consensus is that his teaching quality makes the course highly rewarding.

Recent recorded grades — Fall 2019: 2.87 GPA, 46.0% A/AB (n=50 letter grades); Spring 2020: 3.50 GPA, 73.1% A/AB (n=26 letter grades); Fall 2025: 2.98 GPA, 42.5% A/AB (n=40 letter grades).

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 4.07/5 from 28 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 4.0/5 raw quality · 3.0/5 difficulty · 1 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Benedek Valko provides consistent lecture material and helpful notes, though he can be intimidating and occasionally rude. His grading is not overly harsh, relying on weighted assignments rather than just tests.

Recent recorded grades — Fall 2021: 3.40 GPA, 60.0% A/AB (n=25 letter grades); Spring 2022: 3.33 GPA, 63.4% A/AB (n=41 letter grades); Fall 2025: 3.38 GPA, 48.8% A/AB (n=41 letter grades).

Historical instructors & teaching patterns

Benedek Valko and Timo Seppalainen have no specific reviews in this dataset. Historical reviews highlight varied experiences, with some praising clear instruction and others noting difficulty. Current instructors are not covered by the provided evidence.

BENEDEK VALKO is recorded teaching in Fall 2011, Spring 2012, Fall 2014, Fall 2019, Fall 2021, Spring 2022, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

DANIELE CAPPELLETTI is recorded teaching in Spring 2017, Fall 2017, Spring 2018. Recorded history may be incomplete and does not establish a future schedule.

DAVID KEATING is recorded teaching in Fall 2022, Spring 2023, Fall 2023, Spring 2024. Recorded history may be incomplete and does not establish a future schedule.

DAVID S. GRIFFEATH is recorded teaching in Fall 2007, Spring 2008, Spring 2009, Fall 2009, Spring 2010, Spring 2011. Recorded history may be incomplete and does not establish a future schedule.

ERIK BATES is recorded teaching in Fall 2020, Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

GREGORY SHINAULT is recorded teaching in Spring 2014, Fall 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule.

JUN YIN is recorded teaching in Spring 2013, Spring 2015, Spring 2017. Recorded history may be incomplete and does not establish a future schedule.

SEBASTIEN ROCH is recorded teaching in Fall 2013, Fall 2014, Spring 2015, Fall 2015, Spring 2016, Fall 2016, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

SIGURD ANGENENT is recorded teaching in Fall 2018. Recorded history may be incomplete and does not establish a future schedule.

TIMO SEPPALAINEN is recorded teaching in Spring 2007, Fall 2008, Fall 2018, Fall 2019, Spring 2020, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

XIAOQIN GUO is recorded teaching in Spring 2020. 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction1 / 540
LEC 002Classroom Instruction1 / 540
LEC 001Classroom Instruction5 / 540
LEC 002Classroom Instruction3 / 541
LEC 001Classroom Instruction35 / 540
LEC 002Classroom Instruction43 / 541
LEC 001Classroom Instruction6 / 540
LEC 002Classroom Instruction8 / 540

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Timo Seppalainen receives universal praise for clear, engaging lectures and making complex probability concepts accessible, with students calling him one of the best instructors in the department.

Recent recorded grades — Spring 2025: 3.37 GPA, 66.7% A/AB (n=51 letter grades); Fall 2025: 3.18 GPA, 45.7% A/AB (n=81 letter grades); Spring 2026: 3.01 GPA, 41.1% A/AB (n=56 letter grades).

difficulty & workload

Seppalainen assigns challenging homework that is harder than exams, while Valko uses a grading scheme based on multiple weighted components rather than heavy testing.

Seppalainen is noted for being accommodating with grading formulas, whereas Valko provides helpful notes but is described as intimidating and occasionally unkind.

Topics

  • Discrete-time Markov chains
  • Poisson point processes
  • Continuous-time Markov chains
  • Renewal processes
  • Queueing and branching models

Skills

  • Modeling with Markov chains and point processes
  • Applying stochastic models to real-world systems

Grades

Historical instructor

Fall 2026 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

7 earlier forecasts · 0.14 GPA average error.

Grades over time

Through Fall 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

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.

Download part 1
Selected offering source records
ISYE/MATH/OTM/STAT 632 · Fall 2026

Introduction to Stochastic Processes

Recorded 2026-09-07
ISYE/MATH/OTM/STAT 632 · Fall 2026

Introduction to Stochastic Processes

Recorded 2026-09-07
ISYE/MATH/OTM/STAT 632 · Fall 2026

Introduction to Stochastic Processes

Recorded 2026-09-07
ISYE/MATH/OTM/STAT 632 · Fall 2026

Introduction to Stochastic Processes

Recorded 2026-09-07
Raw records
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Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
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  "output_id": "f7d4d298a9a768f3dd7fe4294e5e66e13611ff79e80ece526060fdad1e944527",
  "requirements_status": "valid",
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