Results 1 to 10 of about 12,131,923 (292)
The statistical model for parton distributions
7 pages, 3 figures, Invited talk at XX International Workshop on "`Deep-Inelastic Scattering and related subjects"', University of Bonn, Germany, March 26-30 2012, presented by F.
Bourrely, Claude +2 more
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Statistical models: an overview [PDF]
Ranstam, J, Cook, J
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Canonical foliations of statistical manifolds with statistical models
Abstract Constructing a realisation of a statistical manifold as a statistical model, i.e a manifold with dual connections with respect to a Fisher metric, is an important question in information geometry. While a positive answer to this problem was given by the work on Hong Van Lé [1], writing explicitly the ...
Gnandi, Emmanuel +2 more
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Data-driven high-dimensional statistical inference with generative models
Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations ...
Oz Amram, Manuel Szewc
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Statistical Models of Ferrimagnetism [PDF]
Syozi, Itiro, Nakano, Huzio
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Comparison of statistical low-frequency earthquake activity models
Slow earthquakes are slow fault slip events. Quantifying and monitoring slow earthquake activity characteristics are important, because they may change before large earthquakes occur.
Tomoaki Nishikawa
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Some of the next articles are maybe not open access.
On Statistical Model Validation
Journal of Dynamic Systems, Measurement, and Control, 1996In this paper we formulate a particular statistical model validation problem in which we wish to determine the probability that a certain hypothesized parametric uncertainty model is consistent with a given input-output data record. Using a Bayesian approach and ideas from the field of hypothesis testing, we show that in many cases of interest this ...
Lee, L. H., Poolla, K.
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2019
In this chapter, we present statistical modelling approaches for predictive tasks in business and science. Most prominent is the ubiquitous multiple linear regression approach where coefficients are estimated using the ordinary least squares algorithm. There are many derivations and generalizations of that technique. In the form of logistic regression,
Dettling, Marcel, Ruckstuhl, Andreas
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In this chapter, we present statistical modelling approaches for predictive tasks in business and science. Most prominent is the ubiquitous multiple linear regression approach where coefficients are estimated using the ordinary least squares algorithm. There are many derivations and generalizations of that technique. In the form of logistic regression,
Dettling, Marcel, Ruckstuhl, Andreas
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Statistics for a Diagnostic Model
Biometrics, 1961In recent years, several methods have been proposed for making medical diagnoses by machine (Ledley and Lusted [1959], Crumb and Rupe [1959]). A method devised by Brodman et al. [1959, 1960] has been used to program a high-speed electronic computer for making presumptive medical diagnoses using only information relating to the age, sex, and responses ...
van Woerkom, Adrianus J., Brodman, Keeve
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2008
Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using ...
Mason, Simon J., Baddour, Omar
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Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using ...
Mason, Simon J., Baddour, Omar
openaire +1 more source

