The Likelihood Principle

By J. Grossman. 2011

245 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6757ddc2d87753473dcc6f58269f2edf0dc4c8ac

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Citations in the corpus

ANCILLARY STATISTICS: A REVIEW (2010, background): “If there is an emerging consensus about the role of ancillarity in statistical inference, it stems from the notion that conditioning on ancillary statistics makes the resulting inference more relevant to the observed data.”
On likelihood ratio tests (2006, background): “As a simple example let the hypothesis H and the alternatives K be specified respectively by H : f(x1 − θ, . . . , xn − θ) and K : g(x1 − θ, . . . , xn − θ) (4.1) where f and g are given densities and θ is an unknown location parameter.”
Coral–macroalgal phase shifts or reef resilience: links with diversity and functional roles of herbivorous fishes on the Great Barrier Reef (2010, background): “…approach that addressed the non-independence of transect-level observations within each study reef and allowed us to make direct inferences on the data observed (Forster et al. 2003), rather than incorrectly assuming our data were replicated in the classical sense (sensu Berger and Wolpert 1988).”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, methodology): “Note that the IT method follows the likelihood principle, which asserts that all relevant information is contained in a likelihood function for the observed data (Berger and Wolpert 1984).”
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes (2020, methodology): “Here we invoke the likelihood principle [1], which is fundamental to Bayesian inference and states that all relevant experimental information is contained in the likelihood function for f given the observed data y.”
On Some Principles of Statistical Inference (2015, methodology): “The use of the strong likelihood principle in the development of Bayesian inference is discussed, with many examples, by Berger & Wolpert (1984).”
Model Comparison and the Principle of Parsimony (2015, methodology): “…of NML depends on the likelihood of data that might have occurred but did not, the procedure violates the likelihood principle, which states that all information about a parameter θ obtainable from an experiment is contained in the likelihood function for θ for the given y (Berger & Wolpert, 1988).”
How Bayes factors change scientific practice (2016, methodology): “Thus, use of Bayes factors respects the ‘‘stopping rule principle’’ according to which the only evidence about a parameter is contained in the data and not the stopping rule used to collect them (Berger & Berry, 1988a,b; Berger & Wolpert, 1988).”