Statistical Models and Methods for Data Science
- Indbinding:
- Paperback
- Sideantal:
- 196
- Udgivet:
- 25. juli 2023
- Udgave:
- 23001
- Størrelse:
- 155x11x235 mm.
- Vægt:
- 306 g.
- 8-11 hverdage.
- 26. november 2024
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- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Statistical Models and Methods for Data Science
This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk.The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9¿11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science.
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Bogen Statistical Models and Methods for Data Science findes i følgende kategorier:
- Business og læring > Computer og IT
- Matematik og naturvidenskab > Matematik > Sandsynlighedsregning og statistik
- Databehandling og informationsteknologi > Virksomhedssoftware > Matematikprogrammer og statistikprogrammer
- Databehandling og informationsteknologi > Databaser
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
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