Not yet published - available from November 2021
FORMAT: HardbackISBN: 9781108831741
Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Introduces deep learning techniques systematically and cohesively, with particular focus on graph neural networks
Extended examples bridge theory and applications in areas such as NLP, vision, datamining, and healthcare
Self-contained, chapters on basic concepts in graph theory and deep learning
1. Deep Learning on Graphs: An Introduction
2. Foundation of Graphs
3. Foundation of Deep Learning
4. Graph Embedding
5. Graph Neural Networks
6. Robust Graph Neural Networks
7. Scalable Graph Neural Networks
8. Graph Neural Networks for Complex Graphs
9. Beyond GNNs: More Deep Models for Graphs
10. Graph Neural Networks in Natural Language Processing
11. Graph Neural Networks in Computer Vision
12. Graph Neural Networks in Data Mining
13. Graph Neural Networks in Biochemistry and Healthcare
14. Advanced Topics in Graph Neural Networks
15. Advanced Applications in Graph Neural Networks.
ISBN 9780367753689 Hard cover
ISBN 9780367753665 Soft cover
September 14, 2021
436 Pages 72 B/W Illustrations
Research in the social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment.
After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions, and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model ? logistic regression ? designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data, and provides simulations in R to test model assumptions.
Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied.
Uses numerous graphs in R to illustrate the modelfs results, assumptions, and other features.
Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient.
Provides many examples using real world datasets relevant to various academic disciplines.
Fully integrates the R software environment in its numerous examples.
The book is aimed primarily at advanced undergraduate and graduate students in the social, behavioral, health sciences, and related disciplines, taking a first course in linear regression. It could also be used for self-study, and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena.
1. Introduction
2. Review of Elementary Statistical Concepts
3. Simple Linear Regression Models
4. Multiple Linear Regression Models
5. The ANOVA Table and Goodness-of-Fit Statistics
6. Comparing Linear Regression Models
7. Indicator Variables in Linear Regression Models
8. Independence
9. Homoscedasticity
10. Collinearity and Multicollinearity
11. Normality, Linearity, and Interaction Effects
12. Model Specification
13. Measurement Errors
14. Influential Observations: Leverage Points and Outliers
15. Multilevel Linear Regression Models
16. A Brief Introduction to Logistic Regression
17. Conclusions
Appendix A: Data Management
Appendix B: Using Simulations to Examine Assumptions of Linear Regression Models
Appendix C: Formulas
Appendix C: User-Written R Packages Employed in Examples
ISBN 9780367538934 Hard cover
ISBN 9780367538910 Soft cover
September 16, 2021
312 Pages
Much of our thinking is flawed because it is based on faulty intuition. By using the framework and tools of probability and statistics, we can overcome this to provide solutions to many real-world problems and paradoxes. We show how to do this, and find answers that are frequently very contrary to what we might expect. Along the way, we venture into diverse realms and thought experiments which challenge the way that we see the world.
An insightful and engaging discussion of some of the key ideas of probabilistic and statistical thinking
Many classic and novel problems, paradoxes, and puzzles
An exploration of some of the big questions involving the use of choice and reason in an uncertain world
The application of probability, statistics, and Bayesian methods to a wide range of subjects, including economics, finance, law, and medicine
Exercises, references, and links for those wishing to cross-reference or to probe further
Solutions to exercises at the end of the book
This book should serve as an invaluable and fascinating resource for university, college, and high school students who wish to extend their reading, as well as for teachers and lecturers who want to liven up their courses while retaining academic rigour. It will also appeal to anyone who wishes to develop skills with numbers or has an interest in the many statistical and other paradoxes that permeate our lives. Indeed, anyone studying the sciences, social sciences, or humanities on a formal or informal basis will enjoy and benefit from this book.
1. Probability, Evidence and Reason
2. Probability Problems and Paradoxes
3. Probability and Choice
4. Probability, Games and Gambling
5. Probability, Truth and Reason
6. Anomalies of Choice and Reason
7. Game Theory and Practice
8. Further Ideas and Exercises
ISBN 9781032072265
August 19, 2021
626 Pages 49 B/W Illustrations
Differential Equations: A Linear Algebra Approach follows an innovative approach of inculcating linear algebra and elementary functional analysis in the backdrop of even the simple methods of solving ordinary differential equations. The contents of the book have been made user-friendly through concise useful theoretical discussions and numerous illustrative examples practical and pathological.
1. A Prelude to Differential Equations 2. Equations of First Order and First Degree 3. A Class of First Order Non-Linear Odes 4. Linear Algebraic Framework in Differential Equations 5. 5 Differential Equations of Higher Order 6. Second Order Linear Ode: Solution Techniques & Qualitative Analysis 7. Laplace Transformations in Ordinary Differential Equations 8. Series Solutions of Linear Differential Equations 9. Solving Linear Systems by Matrix Methods
Hardcover ISBN: 9780323855679
Published Date: 1st September 2021
Page Count: 420
Handbook of Statistics, Volume 45
The subject of information geometry blends several areas of statistics, computer science, physics, and mathematics. The subject evolved from the groundbreaking article published by legendary statistician C.R. Rao in 1945. His works led to the creation of Cramer-Rao bounds, Rao distance, and Rao-Blackawellization. Fisher-Rao metrics and Rao distances play a very important role in geodesics, econometric analysis to modern-day business analytics. The chapters of the book are written by experts in the field who have been promoting the field of information geometry and its applications.
Written by experts for users of information geometry
Basics to advanced readers are equally taken care
Origins and Clarity on Foundations
Statisticians, computer scientists, physicists, econometricians and mathematicians
No. of pages: 420
Language: English
Copyright: c North Holland 2021
Published: 1st September 2021
Imprint: North Holland
Hardcover ISBN: 9780323855679