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1. |
Record Nr. |
UNINA9910300142103321 |
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Autore |
Fischer Matthias J |
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Titolo |
Generalized Hyperbolic Secant Distributions : With Applications to Finance / / by Matthias J. Fischer |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (75 p.) |
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Collana |
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SpringerBriefs in Statistics, , 2191-5458 |
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Disciplina |
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Soggetti |
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Statistics |
Social sciences - Mathematics |
Statistical Theory and Methods |
Statistics in Business, Management, Economics, Finance, Insurance |
Mathematics in Business, Economics and Finance |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Preface -- Hyperbolic Secant Distributions -- The GSH Distribution Family and Skew Versions -- The NEF-GHS or Meixner Distribution Family -- The BHS Distribution Family -- The SHS and SASHS Distribution Family -- Application to Finance -- R-Code: Fitting a BHS Distribution. |
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Sommario/riassunto |
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Among the symmetrical distributions with an infinite domain, the most popular alternative to the normal variant is the logistic distribution as well as the Laplace or the double exponential distribution, which was first introduced in 1774. Occasionally, the Cauchy distribution is also used. Surprisingly, the hyperbolic secant distribution has led a charmed life, although Manoukian and Nadeau had already stated in 1988 that “... the hyperbolic-secant distribution ... has not received sufficient attention in the published literature, and may be useful for students and practitioners.” During the last few years, however, several generalizations of the hyperbolic secant distribution have become popular in the context of financial return data because of its excellent fit. Nearly all of them are summarized within this SpringerBrief. |
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2. |
Record Nr. |
UNINA9910984589503321 |
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Autore |
Zhang Tongyi |
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Titolo |
An Introduction to Materials Informatics : The Elements of Machine Learning / / by Tongyi Zhang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (677 pages) |
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Disciplina |
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Soggetti |
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Materials |
Materials science |
Statistics |
Machine learning |
Materials Engineering |
Materials Science |
Applied Statistics |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Linear Regression -- Linear Classification -- Support Vector Machine -- Decision Tree and K-Nearest-Neighbors (KNN) -- Ensemble Learning -- Bayesian Theorem and Expectation-Maximization (EM) Algorithm -- Symbolic Regression -- Neural Networks -- Hidden Markov Chains -- Data Preprocessing and Feature Selection -- Interpretative SHAP Value and Partial Dependence Plot. |
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Sommario/riassunto |
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This textbook educates current and future materials workers, engineers, and researchers on Materials Informatics. Volume I serves as an introduction, merging AI, ML, materials science, and engineering. It covers essential topics and algorithms in 11 chapters, including Linear Regression, Neural Networks, and more. Suitable for diverse fields like materials science, physics, and chemistry, it enables quick and easy learning of Materials Informatics for readers without prior AI and ML knowledge. |
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