Fall 2026

Probability and Statistical Theory I

MATH/STAT 409 covers the theoretical foundations of statistics, focusing on probability models and large sample theory.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

No student feedback recorded yet.

Grade history

average GPA
letter grades

No recorded grade history.

All recorded terms · compare terms & instructors

Prerequisites

Course map

No prerequisites listed.

  • No prerequisites listed
MATH/STAT 409 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
  • No prerequisites listed

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

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

Provide a grounding in the theory underlying Statistics geared towards understanding why statistical methods work and to critically evaluate their performance. Probability theory highlighting the aspects of the field most relevant to statistical theory. Topics include: probability models, combinatorial methods, discrete and continuous random variables, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, large sample theory and probability simulation.

difficulty & workload

No workload feedback recorded.

Topics

  • Probability models and combinatorial methods
  • Random variables and distributions
  • Expected values, moments, and normal distributions
  • Large sample theory and probability simulation

Skills

  • Critically evaluate statistical performance
  • Apply probability theory to statistical contexts

Grades

Instructor

Grade distribution · % of letter grades

No grades available yet.

More grade details Grade mix, volume & source data

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

No offering records for the selected term.

Raw records
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "29947b3e7f35a5a2f8f152ebb3ff5de0bb9514bf3c930cc25b5a9cfc5d052414",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}