Fall 2026

Methods for Probabilistic Risk Analysis of Nuclear Power Plants

This course teaches methods for probabilistic risk analysis of engineered systems, focusing on fault trees, event trees, Bayesian data analysis, and risk management in the nuclear power industry.

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Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Spring 2016: 3.56 GPA, 75.0% A/AB (n=16 letter grades); Spring 2018: 3.29 GPA, 58.3% A/AB (n=12 letter grades); Spring 2025: 3.85 GPA, 90.0% A/AB (n=10 letter grades).

Grade history

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average GPA
letter grades
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Prerequisites

Course map

(STAT/​MATH 309,STAT 311, 224, 324, or STAT/​MATH 431), graduate/professional standing, or member of Engineering Guest Students

ISYE/NE 574

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Professors

Fall 2026

No instructors recorded for this selection.

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Calendar & sections

Fall 2026

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Student experience

the class

Recent recorded grades — Spring 2016: 3.56 GPA, 75.0% A/AB (n=16 letter grades); Spring 2018: 3.29 GPA, 58.3% A/AB (n=12 letter grades); Spring 2025: 3.85 GPA, 90.0% A/AB (n=10 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Risk and reliability analysis in the nuclear power industry.
  • Fault tree and event tree analysis.
  • Bayesian data analysis.
  • Probabilistic risk management.

Skills

  • Risk and reliability analysis, fault and event tree modeling, Bayesian data analysis, and probabilistic risk management.

Grades

Latest available · Spring 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
B
BC
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F

Grades over time

Through Spring 2025

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Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "8a87540be6ccf8207488640bc86dfb304e23f86ce3a81077091dc5273aa85cc4",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}