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Algorithmic Learning in a Random World
This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.
A Modular Calculus for the Average Cost of Data Structuring
This volume, with forewords by Greg Bollella and Dana Scott, presents novel programs based on the new advances in this area, including the first randomness-preserving version of Heapsort. Programs are provided, along with derivations of their average-case time, to illustrate the radically different approach to average-case timing. The automated static timing tool applies the Modular Calculus to extract the average-case running time of programs directly from their MOQA code.
Maîtriser laléatoire : Exercices résolus de probabilités et statistique = Mastering Randomness : Solved Exercises in Probability and Statistics
Consists of 245 solved exercises that cover all the basic concepts of probability and statistics. The work is structured in nine chapters, each containing a brief introduction, bibliographic references to more specialized works, as well as a series of exercises and their detailed solutions. Ranked in increasing order of difficulty, these will allow the reader to appreciate the extent of his progress. This book can be used as a supplement to any theory manual on statistics and probability. Due to the great diversity of the examples offered, it will suit a diverse readership: students of economics, psychology, social sciences, mathematics, physics, chemistry, medicine or biology.
Chance : The life of games and the game of life
With its many easy-to-follow mathematical examples, this book takes the reader on an almost chronological trip through the fascinating and amazing laws of chance, omnipresent in the natural world and in our daily lives. Along the route many fascinating topics are discussed, such as: challenging probability paradoxes; "paranormal" coincidences; game odds; causes and effects; interpretation of opinion polls; winning chances as a game proceeds; the nature of randomness; entropy and randomness; randomness in life; algorithmic complexity and the undecidability of randomness; possibilities and limitations of learning the laws of a Universe immersed in chance events. This charming book will inform and entertain the scientist and non-scientist alike.
Basic principles and applications of probability theory
This introductory chapter discusses such notions as determinism, chaos and randomness, p- dictibility and unpredictibility, some initial approaches to formalizing r- domness and it surveys certain problems that can be solved by probability theory. This will perhaps give one an idea to what extent the theory can - swer questions arising in speci?c random occurrences and the character of the answers provided by the theory. 1. 1 The Nature of Randomness The phrase “by chance” has no single meaning in ordinary language. For instance, it may mean unpremeditated, nonobligatory, unexpected, and so on. Its opposite sense is simpler: “not by chance” signi?es obliged to or bound to (happen). In philosophy, necessity counteracts randomness. Necessity signi?es conforming to law – it can be expressed by an exact law. The basic laws of mechanics, physics and astronomy can be formulated in terms of precise quantitativerelationswhichmustholdwithironcladnecessity.
