simulation optimization machine learning de pachamanova dessislava (24 résultats)

Etat : New. pp. 720 2nd Edition NO-PA16APR2015-KAP.

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Etat : New. pp. 720.

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Hardcover. Etat : new. Hardcover. A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.Provides a structured introduction to probability, inferential statistics, and data scienceExplores cutting-edge techniques in simulation modeling, optimization, and machine learningDemonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative toolsCovers factor models and stochastic processes in asset pricingIntegrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-makingIs suitable for practitioners, students, and self-learners "A textbook for developing financial risk models using optimization and simulation, with instructions for programming in various languages"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. pp. 720.

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Hardcover. Etat : Brand New. 2nd edition. 720 pages. 10.00x8.00x10.31 inches. In Stock.

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hardcover. Etat : New.

Etat : New. 2025. Hardcover. . . . . .

Etat : As New. Unread book in perfect condition.

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Hardback. Etat : New.

Hardcover. Etat : new. Hardcover. A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.Provides a structured introduction to probability, inferential statistics, and data scienceExplores cutting-edge techniques in simulation modeling, optimization, and machine learningDemonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative toolsCovers factor models and stochastic processes in asset pricingIntegrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-makingIs suitable for practitioners, students, and self-learners "A textbook for developing financial risk models using optimization and simulation, with instructions for programming in various languages"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : New. 2025. Hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Etat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

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Etat : New. Dessislava A. Pachamanova is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology. She is coauthor of Robust Portfolio Optimization and Management and Portfol.

Hardback. Etat : New.

Buch. Etat : Neu. Simulation, Optimization, and Machine Learning for Finance, second edition | Dessislava A. Pachamanova (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | MIT Press Ltd | EAN 9780262049801 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Hardcover. Etat : new. Hardcover. A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making.Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.Provides a structured introduction to probability, inferential statistics, and data scienceExplores cutting-edge techniques in simulation modeling, optimization, and machine learningDemonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative toolsCovers factor models and stochastic processes in asset pricingIntegrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-makingIs suitable for practitioners, students, and self-learners "A textbook for developing financial risk models using optimization and simulation, with instructions for programming in various languages"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Etat : Brand New. 2nd edition. 720 pages. 10.00x8.00x10.31 inches. In Stock.