An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, ...(展开全部)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, a...(展开全部)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, a...(展开全部)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, a...(展开全部)

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Rela...(展开全部)

Perfected over three editions and more than forty years, this field- and classroom-tested reference: * Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. * Treats all the basic and important topics in multivariate statistics. * Adds two new chapters, along with a number of new sections. * Provides the most methodi...(展开全部)

This book presents a concise and rigorous treatment of stochastic calculus. It also gives its main applications in finance, biology and engineering. In finance, the stochastic calculus is applied to pricing options by no arbitrage. In biology, it is applied to populations' models, and in engineering it is applied to filter signal from noise. Not everything is proved, but enough...(展开全部)

This book presents a concise treatment of stochastic calculus and its applications. It gives a simple but rigorous treatment of the subject including a range of advanced topics, it is useful for practitioners who use advanced theoretical results. It covers advanced applications, such as models in mathematical finance, biology and engineering. Self-contained and unified in prese...(展开全部)

This book provides a concise introduction to stochastic calculus with some of its applications in mathematical finance, engineering and the sciences. Applications in finance include pricing of financial derivatives, such as options on stocks, exotic options and interest rate options. The filtering problem and its solution is presented as an application in engineering. Populatio...(展开全部)

罗伯特·塞奇威克（Robert Sedgewick），斯坦福大学博士，导师为Donald E.Knuth,从1985年开始一直担任普林斯顿大学计算机科学系教授，曾任系主任，也是Adobe Systems公司董事会成员，曾在Xerox PARC、国防分析研究所和法国国家信息与电话研究所（INRIA）从事研究工作。他的研究方向包括数据结构和算法的分析与设计、程序可视化等。他同时也是《Java程序设计和算法（第4版）》的作者之一（另一个作者是Kevin Wayne）。 凯文·韦恩（Kevin Wayne），康奈尔大学博士，普林斯顿大学计算机科学系高级讲师。他自1998年开始任教于普林斯顿大学，研究方向包括算法的设计、分析和实现，特别是图和离散化。他是ACM杰出教育家。 罗伯特·唐德罗（Robert Dondero），德雷塞尔大学博士，普林斯顿大学计算机科学...(展开全部)