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Additive and Cancellative Interacting Particle Systems (Lecture Notes in Mathematics) by David Griffeath (Repost)
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Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
Spaces of Holomorphic Functions in the Unit Ball (Graduate Texts in Mathematics) by Kehe Zhu (Repost)
An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
-Mathematics for the Physical Sciences- by Herbert S. Wilf
Mathematics for Elementary Teachers - A Conceptual Approach, 9 edition
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How to Fold It - The Mathematics of Linkages, Origami and Polyhedra
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Mathematics Fundamentals of Algebraic Modeling - An Introduction to Mathematical Modeling with Algebra and Statistics, 5 edition
Mathematics LMSST - 24 Lectures on Elliptic Curves (London Mathematical Society Student Texts) by J. W. S. Cassels
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Measure, Integral and Probability (Springer Undergraduate Mathematics Series)
Posted on 2010-04-28
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Author: Marek Capinski, Ekkehard Kopp
Publisher: Springer-Verlag (1999) Binding: Paperback, 227 pages pricer: $41.95 ISBN-10: 3540762604 editorialreviews Measure Integral and Probability serves as a gentle introduction to subject matter which is central to the foundations of analysis and probability. With an emphasis on clear explanations, and a step-by-step development of basic ideas, the level and style are ideal for advanced undergraduate students. The development of the Lebesgue integral provides the essential ideas; the role of basic convergence theorems, a discussion of modes of convergence for measurable functions, and relations to the Riemann Integral lead to the definition of Lebesgue spaces, the Fubini theorem and their roles in describing the properties of random variables and their distributions. With significant applications to probability, including laws of large numbers and the central limit theorem, this book will also be of interest to postgraduates needing to refresh or enhance their knowledge. Numerous exercises are provided, each with solutions. failed
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