Theoretical characterization of uncertainty in high-dimensional linear classification
Machine Learning: Science and Technology, 2023 Being able to reliably assess not only the accuracy but also the uncertainty of models’ predictions is an important endeavor in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty after ...
Lucas Clarté +3 more
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Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem [PDF]
, 2010 This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish ...
Berger, James O., Scott, James G.
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To be(t) or not to be(t): A Bayesian approach to statistical data analysis [PDF]
EPJ Web of ConferencesThe process of learning from observation is the founding step of Science. When it goes in the direction of collecting empyrical observations and getting to general rules it is called “induction”, and it has the goal to infer from the effects of a given ...
Pisano Silvia
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Remote Sensing, 2018 Remotely sensed data-based models used in operational forest inventory usually give precise and accurate predictions on average, but they often suffer from systematic under- or over-estimation of extreme attribute values resulting in too narrow or skewed
Virpi Junttila, Tuomo Kauranne
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Random and free observables saturate the Tsirelson bound for CHSH inequality [PDF]
, 2017 Maximal violation of the CHSH-Bell inequality is usually said to be a feature of anticommuting observables. In this work we show that even random observables exhibit near-maximal violations of the CHSH-Bell inequality.
Harrow, A. W. +4 more
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Journal of Medical Internet Research, 2020 BackgroundDespite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice.
Kim, Junetae +8 more
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Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models: Uncertainties and probability distribution areas [PDF]
Journal of Marine Systems, 2008 Abstract Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic approaches to the problem are often limited by empirical assumptions on underlying physics.
Rixen, Michel +2 more
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Uncertainty About Uncertainty: What Constitutes “Knowledge of Probability and Statistics Appropriate to the Program Name and Objectives” in our Program Accreditation Criteria [PDF]
2011 ASEE Annual Conference & Exposition Proceedings, 2020 Uncertainty about Uncertainty: what constitutes “knowledge of probability and statistics appropriate to the program name and objectives” in our program accreditation criteriaAbstractEAC of ABET program accreditation criteria for Electrical, Computer, andsimilarly named engineering programs include the requirement that the programmust demonstrate that ...
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An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market [PDF]
, 2005 In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena.
Dionisio, Andreia +2 more
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