Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons

By R. Bradley, M. E. Terry. 1952

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In Statistics and Probability

Citations in the corpus

Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning (2018, methodology): “We use deep learning models to train reward estimators by regression against cardinal feedback, and by fitting a Bradley-Terry model (Bradley and Terry, 1952) to ordinal feedback.”
Life Cycle and Cohort Productivity in Economic Research: The Case of Germany (2008, unknown): “The BT-method ( Bradley and Terry, 1952 ), as applied by Stiegler et al. (1995), is a logit-type model that is used to estimate the odds ratio that one journal will cite another.”
Feature Expansive Reward Learning: Rethinking Human Input (2020, methodology): “sing. Since humans are imperfect, we allow users to violate the monotonicity assumption by modeling them as noisily rational, following the classic Bradley Terry and Luce-Shepard models of preference [14, 15]: P(s˜s0) = P(˚(s) >˚(s0)) = e˚(s) e˚(s) + e˚(s0) : (1) Our method also assumes by defaul”
Preference-based interactive multi-document summarisation (2019, methodology): “The Bradley-Terry (BT) model (Bradley and Terry 1952) is a widely used preference learning model, which approximates the ranking σ∗ x by approximating the utility function U∗ x : Suppose we have observed N preferences: {px(〈y1,1, y1,2〉), · · · , px(〈yN,1, yN,2〉)}, where yi,1, yi,2 ∈ Yx are the summa”
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “ptual-error scores of A and B (denoted by sA and sB , respectively), and computes the probability of preferring A over B : pAB = h(sA ;sB ). In this paper, we use the Bradley-Terry (BT) sigmoid model [9] for h, since it has successfully modeled human responses for pairwise comparisons in other appli”
GROUPED SPARSE PAIRED COMPARISONS IN THE BRADLEY-TERRY MODEL (2011, background): “To describe the probabilities of the possible outcomes in paired comparisons, Bradley and Terry (1952) suggested that the probability subject i beats subject j be specified by pij = ui ui + uj , (1.1) where the “merit vector” (u0, u1, . . . , ut) represents the merit parameters of t+1 subjects.”
The method of Adaptive Comparative Judgement (2012, methodology): “This is also known as the Bradley-Terry-Luce model (Bradley and Terry 1952 ; Luce 1959 ), and it is in this form, rather than Thurstone’s original normal-distribution form, that CJ is generally used today.”
Graphical representations and odds ratios in a distance-association model for the analysis of cross-classified data (2005, background): “Fienberg and Larntz (1976) showed that the logit formulation of the QS-model is the Bradley–Terry model ( Bradley & Terry, 1952 ).”
A no-reference metric for evaluating the quality of motion deblurring (2013, methodology): “We adopt the Bradley-Terry model [Bradley and Terry 1952], which is widely used for this purpose.”
Squibs: Evaluating Human Pairwise Preference Judgments (2015, methodology): “…earliest generally cited models for pairwise comparisons are the Thurstone model (Thurstone 1927) and the closely related Bradley-Terry (BT) model (Bradley and Terry 1952); these have connections to the IRT models, widely used in analyzing responses to questionnaires, which Hopkins and May (2013)…”