Fall 2026

Computational Statistics

STAT 771 teaches computational approaches to statistical inference, covering data reduction, estimation, testing, and modeling via algorithms for optimization and integration.

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

Summary

1 / 5

Historical reviews of Vivak Patel: One review describes lectures as easy to follow and notes that problem sets are optional, with the final exam featuring questions similar to those sets.

Grade history

average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor

  • Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor
STAT 771

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

Prerequisite text tree
  • Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Vivak Patel: Vivak Patel's teaching receives polarized reviews. Some students found him unhelpful and difficult to understand, while others praised his organization, accessibility, and clear lectures focused on practical application.

VIVAK PATEL is recorded teaching in Fall 2018, Fall 2019, Fall 2020, Fall 2021. 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

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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

Historical reviews of Vivak Patel: Reviews for Vivak Patel are polarized, ranging from complaints about poor communication and lack of helpfulness to praise for his organization and accessibility.

Recent recorded grades — Fall 2023: 3.66 GPA, 80.6% A/AB (n=31 letter grades); Fall 2024: 3.70 GPA, 82.6% A/AB (n=23 letter grades); Spring 2026: 3.65 GPA, 76.9% A/AB (n=13 letter grades).

difficulty & workload

Historical reviews of Vivak Patel: One review describes lectures as easy to follow and notes that problem sets are optional, with the final exam featuring questions similar to those sets.

Historical reviews of Vivak Patel: Students report conflicting experiences regarding his helpfulness, with some finding him inaccessible while others describe him as very accessible and encouraging.

Topics

  • Core statistical problems: data reduction, parameter estimation, hypothesis testing, prediction, and statistical modeling
  • Bayesian and non-Bayesian inference methods
  • Computational issues: optimization, standard error computation, and integration for model properties

Skills

  • Implementing computational algorithms for statistical model fitting, optimization, and integration
  • Designing systems for data organization, management, and deployment of statistical computations

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

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

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Selected offering source records

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Raw records
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Student reviews

Original comments behind the course and instructor summaries.

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Grade history

Recorded grade distributions by term, section, and instructor.

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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": "07c2bef905e859d457364c19c2e1f1dc371319dfcdfebaa79a971d672a4af3c9",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}