Linear Algebra and Its Applications (4版)
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書名:Linear Algebra and Its Applications 4/e
作者:STRANG
出版社:Cengage
出版日期:2006/00/00
ISBN:9780030105678
內容簡介
●Offers a large number of applications to physics, engineering, probability and statistics, economics, and biology. These are not tacked on at the end; they are part of the mathematics.
●Includes an optional section on the Fast Fourier Transform. Students discover how this outstanding algorithm fits into linear algebra and introduces complex numbers.
●Recognizes what the computer can do in linear algebra, without being dominated by it.
●Contains a wealth of exercises that appear in the sections and in the chapter reviews.
目錄
1. MATRICES AND GAUSSIAN ELIMINATION.
2. VECTOR SPACES.
3. ORTHOGONALITY.
4. DETERMINANTS.
5. EIGENVALUES AND EIGENVECTORS.
6. POSITIVE DEFINITE MATRICES.
7. COMPUTATIONS WITH MATRICES.
8. LINEAR PROGRAMMING AND GAME THEORY.
Appendix A: Intersection, Sum, and Product of Spaces.
Appendix B: The Jordan Form.
Solutions to Selected Exercises.
原價:
1380
售價:
1311
現省:
69元
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INTRODUCTION TO MATHEMATICAL STATISTICS (8版)
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Introduction to Mathematical Statistics, Global Edition
ISBN13:9781292264769
出版社:Pearson Education Limited
作者:Robert V. Hogg;Joeseph McKean;Allen T. Craig
裝訂/頁數:平裝/760頁
規格:20.7cm*25.5cm*2.1cm (高/寬/厚)
版次:8
出版日:2019/11/05
內容簡介
Comprehensive coverage of mathematical statistics – with a proven approach
Introduction to Mathematical Statistics by Hogg, McKean, and Craig enhances student comprehension and retention with numerous, illustrative examples and exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text’s flexible organization makes it ideal for a range of mathematical statistics courses.
Substantial changes to the 8th Edition – many based on user feedback – help students appreciate the connection between statistical theory and statistical practice, while other changes enhance the development and discussion of the statistical theory presented.
1. Many additional real data sets to illustrate statistical methods or compare methods.
2. Expanded use of the statistical software R, a powerful statistical language which is free and can run on all three main platforms. However, instructors can choose another statistical package if desired.
3. Downloadable, supplemented mathematical review material in Appendix A: reviews sequences, infinite series, differentiation, and integration (univariate and bivariate).
4. Expanded discussion of iterated integrals, with added figures to clarify discussion.
5. A new subsection on the bivariate normal distribution begins the section on the multivariate normal distribution in Chapter 3 (Some Special Distributions).
6. Several important topics have been added, including Tukey’s multiple comparison procedure in Chapter 9 (Inferences About Normal Models)and confidence intervals for the correlation coefficients found in Chapters 9 and 10 (Nonparametric and Robust Statistics).
7. Discussion on standard errors for estimates obtained by bootstrapping the sample is now offered in Chapter 7 (Sufficiency).
Several topics that were discussed in the Exercises are now discussed in the text, including quantiles in Section 1.7.1 and hazard functions in Section 3.3.
目錄
Ch 1 Probability and Distributions
Ch 2 Multivariate Distributions
Ch 3 Some Special Distributions
Ch 4 Some Elementary Statistical Inferences
Ch 5 Consistency and Limiting Distributions
Ch 6 Maximum Likelihood Methods
Ch 7 Sufficiency
Ch 8 Optimal Tests of Hypotheses
Ch 9 Inferences About Normal Models
Ch10 Nonparametric and Robust Statistics
Ch11 Bayesian Statistics
原價:
1460
售價:
1314
現省:
146元
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Linear Algebra and Learning from Data
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Linear Algebra and Learning from Data
ISBN13:9780692196380
出版社:Cambridge Univ Pr
作者:Gilbert Strang
裝訂:精裝
出版日:2019/02/28
內容簡介
●The first textbook designed to teach linear algebra as a tool for deep learning
●From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra
●Includes the necessary background from statistics and optimization
●Explains stochastic gradient descent, the key algorithim of deep learning, in detail
目錄
Deep learning and neural nets
Preface and acknowledgements
Part I. Highlights of Linear Algebra
Part II. Computations with Large Matrices
Part III. Low Rank and Compressed Sensing
Part IV. Special Matrices
Part V. Probability and Statistics
Part VI. Optimization
Part VII. Learning from Data
Books on machine learning
Eigenvalues and singular values: Rank One
Codes and algorithms for numerical linear algebra
Counting parameters in the basic factorizations
原價:
1880
售價:
1748
現省:
132元
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Introduction to Probability and Statistics (15版)
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【簡介】
The skill of statistical thinking is increasing in importance in this predominantly data-driven world. With Mendenhall, Beaver and Beaver's INTRODUCTION TO PROBABILITY AND STATISTICS, 15th Edition, you will be able to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and know what to do when statistical assumptions have been violated.
【目錄】
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Introduction to Probability and Statistics Metric Edition (15版)
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書名:Introduction to Probability and Statistics 15/E (SI)
作者:Mendenhall
出版社:Cengage
出版日期:2019/01/00
ISBN:9780357114469
原價:
1380
售價:
1311
現省:
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An Introduction to Probability and Statistics (3版)
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A MODERN INTRODUCTION TO PROBABILITY AND STATISTICS UNDERSTANDING WHY AND HOW
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