Mathematical Foundations of the Calculus of Probability
Author: Jacques Neveu
Publisher:
Published: 1965
Total Pages: 248
ISBN-13:
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Author: Jacques Neveu
Publisher:
Published: 1965
Total Pages: 248
ISBN-13:
DOWNLOAD EBOOKAuthor: Hans Reichenbach
Publisher:
Published: 1949
Total Pages: 492
ISBN-13:
DOWNLOAD EBOOKAuthor: Hans Reichenbach
Publisher:
Published: 1949
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKAuthor: Jacques Neveu
Publisher:
Published: 1965
Total Pages: 223
ISBN-13:
DOWNLOAD EBOOKAuthor: Alfred Renyi
Publisher: Courier Corporation
Published: 2007-01-01
Total Pages: 386
ISBN-13: 0486462617
DOWNLOAD EBOOKIntroducing many innovations in content and methods, this book involves the foundations, basic concepts, and fundamental results of probability theory. Geared toward readers seeking a firm basis for study of mathematical statistics or information theory, it also covers the mathematical notions of experiments and independence. 1970 edition.
Author: Olav Kallenberg
Publisher: Springer Science & Business Media
Published: 2002-01-08
Total Pages: 670
ISBN-13: 9780387953137
DOWNLOAD EBOOKThe first edition of this single volume on the theory of probability has become a highly-praised standard reference for many areas of probability theory. Chapters from the first edition have been revised and corrected, and this edition contains four new chapters. New material covered includes multivariate and ratio ergodic theorems, shift coupling, Palm distributions, Harris recurrence, invariant measures, and strong and weak ergodicity.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Published: 2020-04-23
Total Pages: 392
ISBN-13: 1108569323
DOWNLOAD EBOOKThe fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Donald Cary Williams
Publisher:
Published: 1951
Total Pages: 6
ISBN-13:
DOWNLOAD EBOOKAuthor: Richard Johnsonbaugh
Publisher: Courier Corporation
Published: 2012-09-11
Total Pages: 450
ISBN-13: 0486134776
DOWNLOAD EBOOKDefinitive look at modern analysis, with views of applications to statistics, numerical analysis, Fourier series, differential equations, mathematical analysis, and functional analysis. More than 750 exercises; some hints and solutions. 1981 edition.
Author: Boris Vladimirovich Gnedenko
Publisher: Courier Corporation
Published: 1962-01-01
Total Pages: 162
ISBN-13: 0486601552
DOWNLOAD EBOOKThis compact volume equips the reader with all the facts and principles essential to a fundamental understanding of the theory of probability. It is an introduction, no more: throughout the book the authors discuss the theory of probability for situations having only a finite number of possibilities, and the mathematics employed is held to the elementary level. But within its purposely restricted range it is extremely thorough, well organized, and absolutely authoritative. It is the only English translation of the latest revised Russian edition; and it is the only current translation on the market that has been checked and approved by Gnedenko himself. After explaining in simple terms the meaning of the concept of probability and the means by which an event is declared to be in practice, impossible, the authors take up the processes involved in the calculation of probabilities. They survey the rules for addition and multiplication of probabilities, the concept of conditional probability, the formula for total probability, Bayes's formula, Bernoulli's scheme and theorem, the concepts of random variables, insufficiency of the mean value for the characterization of a random variable, methods of measuring the variance of a random variable, theorems on the standard deviation, the Chebyshev inequality, normal laws of distribution, distribution curves, properties of normal distribution curves, and related topics. The book is unique in that, while there are several high school and college textbooks available on this subject, there is no other popular treatment for the layman that contains quite the same material presented with the same degree of clarity and authenticity. Anyone who desires a fundamental grasp of this increasingly important subject cannot do better than to start with this book. New preface for Dover edition by B. V. Gnedenko.