Fall 2026

Statistical Methods for Spatial Data

STAT 575 teaches methods for detecting, quantifying, and modeling spatial patterns, including variograms, covariance functions, and Gaussian processes.

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Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Fall 2015: 3.66 GPA, 91.2% A/AB (n=34 letter grades); Spring 2024: 3.31 GPA, 66.7% A/AB (n=27 letter grades); Fall 2025: 3.33 GPA, 61.1% A/AB (n=18 letter grades).

Grade history

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average GPA
letter grades
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Prerequisites

Course map

(STAT 333 or 340) and (MATH 320, 340, 341, 345, or 375), graduate/professional standing, or declared in Statistics VISP

STAT 575

This is a best-effort interpretation; check the catalog requirements above.

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Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Student experience

the class

Recent recorded grades — Fall 2015: 3.66 GPA, 91.2% A/AB (n=34 letter grades); Spring 2024: 3.31 GPA, 66.7% A/AB (n=27 letter grades); Fall 2025: 3.33 GPA, 61.1% A/AB (n=18 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Variograms and covariance functions
  • Spectral domain models
  • Spatial point processes
  • Gaussian process model fitting

Skills

  • Detecting and quantifying spatial patterns
  • Modeling spatial structure with variograms and covariance functions
  • Analyzing spectral domain models
  • Modeling spatial point processes
  • Fitting Gaussian process models at scale

Grades

Latest available · Fall 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
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BC
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D
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Grades over time

Through Fall 2025

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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": "71c4502c815b1d6596c2e714c9ad1e5a590da9b5761ee83a0181847c82154699",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}