We want to believe that serendipity brings us together, but is that just a myth? Mining the comedy of missed connections, THIS RANDOM WORLD asks the serious question of how often we travel parallel paths through the world without noticing. From an ailing woman who plans one final trip, to her daughter planning one great escape and her son falling prey to a prank gone wrong, this funny, intimate, and heartbreaking play explores the lives that may be happening just out of reach of our own.
This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded.
Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions
"The idea for the book came initially from my research notes/ internal blog ... [which] deals mainly with the changing nature of investment strategy of major global funds, the aftermath of the global financial crisis, the impact of Brexit, the global position of London, the increasingly political nature of global capital flows into the sector and the industry's default position which is one of delusion and a hope against hope for soft landings... These issues are set against a background of just how the real estate capital market has evolved ... from a local and rather dozy industry to one of increasing sophistication. However, the underlying argument is that this apparent shift is superficial despite the hope that, somehow, bankers have learnt from the lessons of the past, that rating agencies and regulators do more than tick boxes, or investors cease their natural tendency to stray from the world they know best." Joe Valente, June 2018 All profits from this book will be donated to Bloodwise.
• Starts from the basics, focusing less on proofs and the high-level math underlying regressions, and adopts an engaging tone to provide a text which is entirely accessible to students who don’t have a stats background • New chapter on integrity and ethics in regression analysis • Each chapter offers boxed examples, stories, exercises and clear summaries, all of which are designed to support student learning • Optional appendix of statistical tools, providing a primer to readers who need it • Code in R and Stata, and data sets and exercises in Stata and CSV, to allow students to practice running their own regressions • Author-created videos on YouTube • PPT lecture slides and test bank for instructors
The "Art of Life" is John Stuart Mill's name for his account of practical reason. In this volume, eleven leading scholars elucidate this fundamental, but widely neglected, element of Mill's thought. Mill divides the Art of Life into three "departments": "Morality, Prudence or Policy, and Aesthetics." In the volume's first section, Rex Martin, David Weinstein, Ben Eggleston, and Dale E. Miller investigate the relation between the departments of morality and prudence. Their papers ask whether Mill is a rule utilitarian and, if so, whether his practical philosophy must be incoherent. The second section contains papers by Jonathan Riley and Wendy Donner, who explore the relation between the departments of morality and aesthetics. They discuss issues ranging from supererogation to aesthetic pleasure and humanity's relationship with nature. The papers in the third section consider the Art of Life's axiological first principle, the principle of utility. Elijah Millgram contends that Mill's own life refutes his claim that the Art of Life has a single axiological first principle. Philip Kitcher maintains that Mill has a dynamic axiology requiring us to continually refine our conception of the good. In the final section, three papers address what it means to put the Art of Life into practice. Robert Haraldsson locates an 'Art of Ethics' in On Liberty that is in tension with the Art of Life. Nadia Urbinati plumbs the classical roots of Mill's view of the good life. Finally, Colin Heydt develops Mill's suggestion that we regard our own lives as works of art.
How to use math to improve performance and predict outcomes in professional sports Mathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren’t enough to determine who’s the NBA’s best player; and more.
Like a pianist who practices from a book of tudes, readers of Programming Projects in C for Students of Engineering, Science, and Mathematics will learn by doing. Written as a tutorial on how to think about, organize, and implement programs in scientific computing, this book achieves its goal through an eclectic and wide-ranging collection of projects. Each project presents a problem and an algorithm for solving it. The reader is guided through implementing the algorithm in C and compiling and testing the results. It is not necessary to carry out the projects in sequential order. The projects?contain suggested algorithms and partially completed programs for implementing them to enable the reader to exercise and develop skills in scientific computing;?require only a working knowledge of undergraduate multivariable calculus, differential equations, and linear algebra; and?are written in platform-independent standard C, and the Unix command-line is used to illustrate compilation and execution. The primary audience of this book is graduate students in mathematics, engineering, and the sciences. The book will also be of interest to advanced undergraduates and working professionals who wish to exercise and hone their skills in programming mathematical algorithms in C. A working knowledge of the C programming language is assumed.
Everyone knows the small-world phenomenon. Watts uses this intriguing phenomenon--colloquially called "six degrees of separation"--as a prelude to a more general exploration: under what conditions can a small world arise in any kind of network?
