Results 11 to 20 of about 17,665,845 (300)
Instance-Based Classification Through Hypothesis Testing
IEEE Access, 2021 Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as
Zengyou He +3 more
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Marginal likelihood computation for model selection and hypothesis testing: an extensive review [PDF]
SIAM Review, 2020 This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications ...
F. Llorente +3 more
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Testing the Omnivore Hypothesis in Russia
Мониторинг общественного мнения: экономические и социальные перемены, 2021 In the last thirty years, a significant shift from the homology to omnivore argument has occurred in musical preference studies. Studies on the omnivore argument mainly come from North and South America, Western and sometimes Eastern Europe.
Юлия Олеговна Папушина
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MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction
bioRxiv, 2021 Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real ...
O. Menyhárt, B. Weltz, Balázs Győrffy
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Privacy-Aware Distributed Hypothesis Testing
Entropy, 2020 A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless source, and communicates its observations to the detector via a rate-limited
Sreejith Sreekumar +2 more
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Current controversies: Null hypothesis significance testing
Acta Obstetricia et Gynecologica Scandinavica, 2022 Traditional null hypothesis significance testing (NHST) incorporating the critical level of significance of 0.05 has become the cornerstone of decision‐making in health care, and nowhere less so than in obstetric and gynecological research. However, such
Philip M. Sedgwick +3 more
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Score-Based Hypothesis Testing for Unnormalized Models
IEEE Access, 2022 Unnormalized statistical models play an important role in machine learning, statistics, and signal processing. In this paper, we derive a new hypothesis testing procedure for unnormalized models. Our approach is motivated by the success of score matching
Suya Wu +4 more
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Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence
Nature Neuroscience, 2020 Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect.
C. Keysers, V. Gazzola, E. Wagenmakers
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Random effects structure for confirmatory hypothesis testing: Keep it maximal
Journal of Memory and Language, 2013 Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis.
D. Barr +3 more
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Quantum hypothesis testing in many-body systems
SciPost Physics Core, 2021 One of the key tasks in physics is to perform measurements in order to determine the state of a system. Often, measurements are aimed at determining the values of physical parameters, but one can also ask simpler questions, such as "is the system in ...
Jan de Boer, Victor Godet, Jani Kastikainen, Esko Keski-Vakkuri
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