পদ্ধতির তুলনা করুন

	ব্র্যাডলি-ট্যারি মডেল	মাল্টিনোমিয়াল লজিস্টিক রিগ্রেশন	র‍্যাঙ্ক একত্রীকরণ পদ্ধতি
ক্ষেত্র	সিদ্ধান্ত গ্রহণ	অর্থমিতি	সিদ্ধান্ত গ্রহণ
পরিবার	Regression model	Regression model	Machine learning
উদ্ভবের বছর	1952	1974	2001
প্রবর্তক	Ralph Bradley & Milton Terry	McFadden	Dwork, Kumar, Naor & Sivakumar
ধরন	Probabilistic paired comparison model	Multinomial logistic regression	Combinatorial ranking method
মৌলিক উৎস	Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613–622.
অপর নাম	BT Model, Bradley-Terry-Luce Model, Paired Comparison Model, İkili Karşılaştırma Modeli	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	Rank Fusion, Order Aggregation, Preference Aggregation, Sıralama Birleştirme
সম্পর্কিত	3	5	2
সারসংক্ষেপ	The Bradley-Terry model is a probabilistic model for paired comparisons that assigns a latent strength parameter to each item and predicts the probability that one item beats another in a head-to-head contest. Introduced by Ralph A. Bradley and Milton E. Terry in 1952, it provides a principled statistical framework for ranking items from pairwise preference data, including incomplete comparison designs where not every pair is directly observed.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	Rank Aggregation is a family of methods that combine multiple ranked lists of alternatives into a single consensus ranking. Formally studied in the context of web search by Dwork, Kumar, Naor, and Sivakumar (2001), these methods address the problem of synthesizing divergent preference orderings from multiple sources — such as search engines, expert judges, or voter ballots — into one coherent, representative ordering that minimizes overall disagreement across the input rankings.