ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1305.0015
  4. Cited By
Inferring ground truth from multi-annotator ordinal data: a
  probabilistic approach

Inferring ground truth from multi-annotator ordinal data: a probabilistic approach

30 April 2013
Balaji Lakshminarayanan
Yee Whye Teh
ArXiv (abs)PDFHTML

Papers citing "Inferring ground truth from multi-annotator ordinal data: a probabilistic approach"

1 / 1 papers shown
Title
How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical
  Model for Adaptive Crowdsourcing and Aptitude Testing
How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach
T. Graepel
T. Minka
J. Guiver
142
210
0
27 Jun 2012
1