株式会社極東書店トップ ＞ 商品一覧 ＞ Financial Data and Artificial Intelligence. Volume I : An Introduction to Computational Statistics, Networks, Algorithms, Multivariate Probability Systems, and Bayesian and Kalman-Filtering Analysis.
商品詳細
Financial Data and Artificial Intelligence. Volume I : An Introduction to Computational Statistics, Networks, Algorithms, Multivariate Probability Systems, and Bayesian and Kalman-Filtering Analysis.
・ISBN 978-3-030-75042-8 hard
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| 著者・編者 | Tapiero, Charles S. / Tapiero, Oren J., |
|---|---|
| 出版社 | (Palgrave Macmillan, UK) |
| 出版年月 | 2021.11 |
| ページ数 | 421 pp. |
| 言語 | ENG |
| ニュース番号 | ＜664-829＞ |
解説
The growth of financial complexity, technology, and big data is transforming and integrating computational statistics and data science; in their wake, it's also changing financial engineering. This first volume introduces elements of computational statistics and data algorithms and considers conventional financial models using statistical models. Such a method provides a more transparent approach to data-science methods when applied to financial data.
This book focuses on financial data including time series, default models, and their increasing complexity in a technological and global financial world. It outlines elements of computational statistics and features applications, including problems and models of credit risks and time series applied to various financial problems. Based on multiple sources, academic research, and applications drawn from various domains and adapted to financial data, this book will be of interest to financial engineering researchers, students, and practitioners.
This book focuses on financial data including time series, default models, and their increasing complexity in a technological and global financial world. It outlines elements of computational statistics and features applications, including problems and models of credit risks and time series applied to various financial problems. Based on multiple sources, academic research, and applications drawn from various domains and adapted to financial data, this book will be of interest to financial engineering researchers, students, and practitioners.