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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.Key Features:Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.Includes supplements and exercises to facilitate deeper comprehension. 464 pp. Englisch.
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tze Leung Lai is a professor of statistics at Stanford University in California. Haipeng Xing is an assistant professor of applied mathematics and statistics at the State University of New York, Stony Brook.Tze Leung Lai is the R.
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.Key Features:Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.Includes supplements and exercises to facilitate deeper comprehension.
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Aggiungi al carrelloBuch. Condizione: Neu. Data Science and Risk Analytics in Finance and Insurance | Tze Leung Lai (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | CRC Press | EAN 9781439839485 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.