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Washington University in St. Louis

Speeding up the quantification of Contrast Sensitivity functions using Multidimensional Bayesian Active Learning

dc:creator Shaffiey, Shohaib

thesis:degree_name Master of Science (MS)

dc:date.available2022

Degree

thesis:*

Name thesis:degree_name: Master of Science (MS)
Level thesis:degree_level: Thesis
Discipline thesis:degree_discipline: Computer Science & Engineering
Year dc:date.available: 2022

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Shaffiey, Shohaib
Contributors dc:contributor: Dr. Dennis Barbour
Dr. Roman Garnett Dr. Alvitta Ottley

Subjects

dc:subject × 2

Rights

dc:rights

Statement dc:rights: I have not registered my thesis with the U.S. Copyright Office, and do not intend to.
Language dc:language: English (en)

Identifiers

dc:identifier.*

Identifier: https://openscholarship.wustl.edu/eng_etds/755
OAI identifier oai:identifier: oai:openscholarship.wustl.edu:eng_etds-1816

Chain of custody

source

Harvested from: Washington University in St. Louis
Base URL: openscholarship.wustl.edu/do/oai/
Last updated: 2026-07-24
Source record: OAI-PMH GetRecord

related terms

citation

Shaffiey, Shohaib. Speeding up the quantification of Contrast Sensitivity functions using Multidimensional Bayesian Active Learning. Thesis thesis, 2022. https://doi.org/10.7936/bgmn-nh46

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