A first course in random matrix theory : for physicists, engineers and data scientists
著者
書誌事項
A first course in random matrix theory : for physicists, engineers and data scientists
Cambridge University Press, 2021
- : hardback
並立書誌 全1件
大学図書館所蔵 件 / 全9件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists.
目次
- Preface
- Part I. Classical Random Matrix Theory: 1. Deterministic Matrices
- 2. Wigner Ensemble and Semi-circle Law
- 3. More on Gaussian Matrices
- 4. Wishart Ensemble and Marcenko-Pastur Distribution
- 5. Joint Distribution of Eigenvalues
- 7. The Jacobi Ensemble
- Part II. Sums and Products of Random Matrices: 8. Addition of Random Variables and Brownian Motion
- 9. Dyson Brownian Motion
- 10. Addition of Large Random Matrices
- 11. Free Probabilities
- 12. Free Random Matrices
- 13. The Replica Method
- 14. Edge Eigenvalues and Outliers
- Part III. Applications: 15. Addition and Multiplication: Recipes and Examples
- 16. Products of Many Random Matrices
- 17. Sample Covariance Matrices
- 18. Bayesian Estimation
- 19. Eigenvector Overlaps and Rotationally Invariant Estimators
- 20. Applications to Finance
- Appendix A. Appendices: Mathematical Tools
- List of Symbols
- Index.
「Nielsen BookData」 より
