Author: Elisabetta AlvoniPublisher:
ISBN:
Category :
Languages : it
Pages : 70
Book Description
Approssimazione lineare (parte 1), programmazione lineare
Author: Elisabetta AlvoniProgrammazione lineare
Author: Claudio NapoleoniLa programmazione lineare
Author: Luigi PoiagaLatex in 157 Minutes
Author: Tobias OetikerPublisher: Samurai Media Limited
ISBN: 9789881443625
Category : Computers
Languages : en
Pages : 172
Book Description
Latex is a typesetting system that is very suitable for producing scientific and mathematical documents of high typographical quality. It is also suitable for producing all sorts of other documents, from simple letters to complete books. Latex uses Tex as its formatting engine. This short introduction describes Latex and should be sufficient for most applications of Latex.
Household Food Consumption Survey
Author: United States. Department of AgriculturePublisher:
ISBN:
Category : Diet
Languages : en
Pages : 198
Book Description
Principles of Copula Theory
Author: Fabrizio DurantePublisher: CRC Press
ISBN: 1439884447
Category : Mathematics
Languages : en
Pages : 331
Book Description
This book gives readers the solid and formal mathematical background to apply copulas to a range of mathematical areas, such as probability, real analysis, measure theory, and algebraic structures. The authors prove the results as simply as possible and unify various methods scattered throughout the literature in common frameworks, including shuffles of copulas. They also explore connections with related functions, such as quasi-copulas, semi-copulas, and triangular norms, that have been used in different domains.
Dependence Modeling
Author: Harry JoePublisher: World Scientific
ISBN: 981429988X
Category : Business & Economics
Languages : en
Pages : 370
Book Description
1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka
The Work of Art
Author: Gérard GenettePublisher: Cornell University Press
ISBN: 9780801482724
Category : Art
Languages : en
Pages : 292
Book Description
What art is--its very nature--is the subject of this book by one of the most distinguished continental theorists writing today. Informed by the aesthetics of Nelson Goodman and referring to a wide range of cultures, contexts, and media, The Work of Art seeks to discover, explain, and define how art exists and how it works. To this end, Gérard Genette explores the distinction between a work of art's immanence--its physical presence--and transcendence--the experience it induces. That experience may go far beyond the object itself.Genette situates art within the broad realm of human practices, extending from the fine arts of music, painting, sculpture, and literature to humbler but no less fertile fields such as haute couture and the culinary arts. His discussion touches on a rich array of examples and is bolstered by an extensive knowledge of the technology involved in producing and disseminating a work of art, regardless of whether that dissemination is by performance, reproduction, printing, or recording. Moving beyond examples, Genette proposes schemata for thinking about the different manifestations of a work of art. He also addresses the question of the artwork's duration and mutability.
Mathematical Modeling in Economics and Finance: Probability, Stochastic Processes, and Differential Equations
Author: Steven R. DunbarPublisher: American Mathematical Soc.
ISBN: 1470448394
Category : Business & Economics
Languages : en
Pages : 250
Book Description
Mathematical Modeling in Economics and Finance is designed as a textbook for an upper-division course on modeling in the economic sciences. The emphasis throughout is on the modeling process including post-modeling analysis and criticism. It is a textbook on modeling that happens to focus on financial instruments for the management of economic risk. The book combines a study of mathematical modeling with exposure to the tools of probability theory, difference and differential equations, numerical simulation, data analysis, and mathematical analysis. Students taking a course from Mathematical Modeling in Economics and Finance will come to understand some basic stochastic processes and the solutions to stochastic differential equations. They will understand how to use those tools to model the management of financial risk. They will gain a deep appreciation for the modeling process and learn methods of testing and evaluation driven by data. The reader of this book will be successfully positioned for an entry-level position in the financial services industry or for beginning graduate study in finance, economics, or actuarial science. The exposition in Mathematical Modeling in Economics and Finance is crystal clear and very student-friendly. The many exercises are extremely well designed. Steven Dunbar is Professor Emeritus of Mathematics at the University of Nebraska and he has won both university-wide and MAA prizes for extraordinary teaching. Dunbar served as Director of the MAA's American Mathematics Competitions from 2004 until 2015. His ability to communicate mathematics is on full display in this approachable, innovative text.
Hands-On Machine Learning with R
Author: Brad BoehmkePublisher: CRC Press
ISBN: 1000730433
Category : Business & Economics
Languages : en
Pages : 374
Book Description
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.