The.Hottest

Karl Hinderer, Ulrich Rieder, Michael Stieglitz ... 530 pages - Publisher: Springer; 1st edition (January, 2017) ... Language: English - ISBN-10: 3319488139 - ISBN-13: 978-3319488134 ...

This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

Joseph P. Bigus ... 220 pages - Publisher: Mcgraw-Hill; (May, 1996) ... Language: English - ISBN-10: 0070057796 - ISBN-13: 978-0070057791 ...

Readers will find concrete implementation strategies, reinforced with real-world business examples and a minimum of formulas, and case studies drawn from a broad range of industries. The book illustrates the popular data mining functions of classification, clustering, modeling, and time-series forecasting--through examples developed using the IBM Neural Network Utility.

Simon Haykin ... 842 pages - Publisher: Prentice Hall; 2nd edition (July, 1998) ... Language: English - ISBN-10: 0132733501 - ISBN-13: 978-0132733502 ...

This text represents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Neural Networks provides broad coverage of the subject, including the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementations. Chapter objectives, computer experiments, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary reinforce key concepts. The author's concise and fluid writing style makes the material more accessible. For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

W. T. Ziemba ... 756 pages - Publisher: World Scientific Publishing Company; (September, 2006) ... Language: English - ISBN-10: 981256800X - ISBN-13: 978-9812568007 ...

A reprint of one of the classic volumes on portfolio theory and investment, this book has been used by the leading professors at universities such as Stanford, Berkeley, and Carnegie-Mellon. It contains five parts, each with a review of the literature and about 150 pages of computational and review exercises and further in-depth, challenging problems.Frequently referenced and highly usable, the material remains as fresh and relevant for a portfolio theory course as ever.

Paul Attewell, David Monaghan ... 264 pages - Publisher: Univ. of California Press; (May, 2015) ... Language: English - ISBN-10: 0520280989 - ISBN-13: 978-0520280984 ...

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

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