Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Da: SMASS Sellers, IRVING, TX, U.S.A.
Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Da: SMASS Sellers, IRVING, TX, U.S.A.
Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
EUR 126,32
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. pp. 632.
Condizione: New. pp. 632.
EUR 129,25
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. pp. 632.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10: 3031348710 ISBN 13: 9783031348716
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientistsis designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Okt 2024, 2024
ISBN 10: 3031348710 ISBN 13: 9783031348716
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientistsis designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. 632 pp. Englisch.
Da: moluna, Greven, Germania
EUR 89,99
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Da: Majestic Books, Hounslow, Regno Unito
EUR 150,99
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 153,57
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031348680 ISBN 13: 9783031348686
Da: moluna, Greven, Germania
EUR 124,20
Quantità: Più di 20 disponibili
Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, r.