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Author: Svetlozar T. Rachev
Publisher: Springer Science & Business Media
ISBN: 1461448697
Category : Mathematics
Languages : en
Pages : 616
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Book Description
This book covers the method of metric distances and its application in probability theory and other fields. The method is fundamental in the study of limit theorems and generally in assessing the quality of approximations to a given probabilistic model. The method of metric distances is developed to study stability problems and reduces to the selection of an ideal or the most appropriate metric for the problem under consideration and a comparison of probability metrics. After describing the basic structure of probability metrics and providing an analysis of the topologies in the space of probability measures generated by different types of probability metrics, the authors study stability problems by providing a characterization of the ideal metrics for a given problem and investigating the main relationships between different types of probability metrics. The presentation is provided in a general form, although specific cases are considered as they arise in the process of finding supplementary bounds or in applications to important special cases. Svetlozar T. Rachev is the Frey Family Foundation Chair of Quantitative Finance, Department of Applied Mathematics and Statistics, SUNY-Stony Brook and Chief Scientist of Finanlytica, USA. Lev B. Klebanov is a Professor in the Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic. Stoyan V. Stoyanov is a Professor at EDHEC Business School and Head of Research, EDHEC-Risk Institute—Asia (Singapore). Frank J. Fabozzi is a Professor at EDHEC Business School. (USA)
Author: Svetlozar T. Rachev
Publisher: Springer Science & Business Media
ISBN: 1461448697
Category : Mathematics
Languages : en
Pages : 616
Get Book Here
Book Description
This book covers the method of metric distances and its application in probability theory and other fields. The method is fundamental in the study of limit theorems and generally in assessing the quality of approximations to a given probabilistic model. The method of metric distances is developed to study stability problems and reduces to the selection of an ideal or the most appropriate metric for the problem under consideration and a comparison of probability metrics. After describing the basic structure of probability metrics and providing an analysis of the topologies in the space of probability measures generated by different types of probability metrics, the authors study stability problems by providing a characterization of the ideal metrics for a given problem and investigating the main relationships between different types of probability metrics. The presentation is provided in a general form, although specific cases are considered as they arise in the process of finding supplementary bounds or in applications to important special cases. Svetlozar T. Rachev is the Frey Family Foundation Chair of Quantitative Finance, Department of Applied Mathematics and Statistics, SUNY-Stony Brook and Chief Scientist of Finanlytica, USA. Lev B. Klebanov is a Professor in the Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic. Stoyan V. Stoyanov is a Professor at EDHEC Business School and Head of Research, EDHEC-Risk Institute—Asia (Singapore). Frank J. Fabozzi is a Professor at EDHEC Business School. (USA)
Author: Susan Page
Publisher: RosettaBooks
ISBN: 0795334435
Category : Business & Economics
Languages : en
Pages : 333
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Book Description
“The most thorough, accurate, user-friendly, well-organized and inspiring guide for writers on the market today. Period.”—Richard Carlson, #1 New York Times bestselling author of Don’t Sweat the Small Stuff This expert guide has put the dream of acquiring a publisher within reach for thousands of writers. Whether your book idea is a completed manuscript or still in the planning stages, The Shortest Distance Between You and a Published Book offers comprehensive, industry-savvy guidance on the steps to take to sell your book to a major publisher. Literary agents often advise their clients to read this book as their first step. Susan Page is the author of several bestselling self-help books, and a veteran of the publishing industry. Here, she’ll guide you step-by-step through the roadblocks that stall other writers and help you toward a publishing strategy that gets results. You’ll find in-depth information on the early steps to take, writing title ideas, developing winning book proposals, finding an agent, understanding publishing contracts, promoting your book, and more. Throughout the process, Page coaches you through both the emotional and practical obstacles you’re likely to face. It’s a must-read for anyone interested in a career as a published author. “Page, as her subtitle claims, really does tell you what you need to know to get happily published. This self-help author (If I’m So Wonderful, Why Am I Still Single?) knows what she’s talking about, whether she’s advising on how to write a book proposal, find an agent or promote one’s book . . . This is one of the more instructive guides to read before writing your book.”—Publishers Weekly
Author: Sir Richard PHILLIPS
Publisher:
ISBN:
Category :
Languages : en
Pages : 656
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Book Description
Author: Svetlozar T. Rachev
Publisher: John Wiley & Sons
ISBN: 1444392700
Category : Business & Economics
Languages : en
Pages : 264
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Book Description
A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time. Helps to answer the question: which risk measure is best for a given problem? Finds new relations between existing classes of risk measures Describes applications in finance and extends them where possible Presents the theory of probability metrics in a more accessible form which would be appropriate for non-specialists in the field Applications include optimal portfolio choice, risk theory, and numerical methods in finance Topics requiring more mathematical rigor and detail are included in technical appendices to chapters
Author:
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ISBN:
Category : Great Britain
Languages : en
Pages : 586
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Book Description
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Publisher:
ISBN:
Category : History
Languages : en
Pages : 584
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 584
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Author: Olfa Nasraoui
Publisher: Springer
ISBN: 3540774858
Category : Computers
Languages : en
Pages : 259
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Book Description
Web mining and usage is a fast-moving and hugely important field of study. This new Springer text constitutes the thoroughly refereed post-proceedings of the 8th International Workshop on Mining Web Data, WEBKDD 2006, held in Philadelphia, USA in 2006. The 13 revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were carefully selected for inclusion in the book. They cover a huge range of relevant topics.
Author: Masatoshi Nei
Publisher: Oxford University Press
ISBN: 0199881227
Category : Science
Languages : en
Pages : 444
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Book Description
During the last ten years, remarkable progress has occurred in the study of molecular evolution. Among the most important factors that are responsible for this progress are the development of new statistical methods and advances in computational technology. In particular, phylogenetic analysis of DNA or protein sequences has become a powerful tool for studying molecular evolution. Along with this developing technology, the application of the new statistical and computational methods has become more complicated and there is no comprehensive volume that treats these methods in depth. Molecular Evolution and Phylogenetics fills this gap and present various statistical methods that are easily accessible to general biologists as well as biochemists, bioinformatists and graduate students. The text covers measurement of sequence divergence, construction of phylogenetic trees, statistical tests for detection of positive Darwinian selection, inference of ancestral amino acid sequences, construction of linearized trees, and analysis of allele frequency data. Emphasis is given to practical methods of data analysis, and methods can be learned by working through numerical examples using the computer program MEGA2 that is provided.
Author: Adam Schenker
Publisher: World Scientific
ISBN: 9812563393
Category : Computers
Languages : en
Pages : 250
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Book Description
This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance ? a relatively new approach for determining graph similarity ? the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms.To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters.In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling.
