Bayes factors

By Robert E. Kass, Adrian E. Raftery. 1995

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Citations in the corpus

Prioritizing network communities (2018, methodology): “The calculation is based on Bayes factors 36 – 38 , an established tool in statistics.”
Learning in Implicit Generative Models (2016, methodology): “The comparator r ( x ) provides information about the departure of our model distribution from the true distribution and can be motivated by either the Neyman-Pearson lemma or the Bayesian posterior evidence, appearing in the likelihood ratio and the Bayes factor (Kass and Raftery, 1995).”
Bayesian Deep Learning and a Probabilistic Perspective of Generalization (2020, methodology): “The marginal likelihood is often used for Bayesian hypothesis testing, model comparison, and hyperparameter tuning, with Bayes factors used to select between models [18].”
Constraints on the sum of the neutrino masses in dynamical dark energy models with w(z)≥−1 are tighter than those obtained in ΛCDM (2018, methodology): “These values show a mild preference for the NO model, using the scale provided by [253].”
J. B. S. Haldane's Contribution to the Bayes Factor Hypothesis Test (2015, background): “In their influential introductory paper on Bayes factors, Kass and Raftery (1995) state,”
Pattern Recognition and Machine Learning (2006, unknown): “The ratio of model evidences p(D|Mi)/p(D|Mj) for two models is known as a Bayes factor (Kass and Raftery, 1995).”
Bayesian Model Selection and Model Averaging. (2000, background): “From (25) and the definition of B ij we observe that log B ij rl i &l j + d j &d i 2 , log n, (27) which is also known as BIC or the Schwarz criterion (Schwarz, 1978; Kass and Raftery, 1995).”