Probability measure (749 Ergebnisse)
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Hard cover. First edition. Sewn binding. Cloth over boards. 208 p. Contains: Illustrations, black & white. Audience: General/trade. Very good in fine dust jacket. previous owner name on first page is only flaw, excellent jacket.
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Paperback. Zustand: new. Paperback. This text provides undergraduate mathematics students with an introduction to the modern theory of probability as well as the roots of the theory's mathematical ideas and techniques. Centered around the concept of measure and integration, the treatment is applicable to other branches of analys…is and explores more specialized topics, including convergence theorems and random sequences and functions.The initial part is devoted to an exploration of measure and integration from first principles, including sets and set functions, general theory, and integrals of functions of real variables. These topics provide tools for use in the second part, which emphasizes underlying mathematical ideas, including the roles of random variables and limit processes in probability. The concise format and exposition offer an ideal review of the subject for students with some background in probability. Introductory treatment develops the theory of integration in a general context, making it applicable to other branches of analysis. More specialized topics include convergence theorems and random sequences and functions. 1963 edition. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Sprache: Englisch
Verlag: Springer (edition 2nd), 2004
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Sprache: Englisch
Verlag: Springer, 2004
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Paperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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Sprache: Englisch
Verlag: Secaucus, New Jersey, U.S.A.: Springer Verlag, 1999
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Zustand: very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.
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Zustand: New. 2nd Edition.
Sprache: Englisch
Verlag: Secaucus, New Jersey, U.S.A.: Sp, 1999
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paperback. Zustand: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.
Sprache: Englisch
Verlag: Hindustan Book Agency, 2005
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Soft cover. Zustand: As New. Contents Preface. 1. Probability on Boolean algebras. 2. Extension of measures. 3. Borel maps. 4. Integration. 5. Measures on product spaces. 6. Hilbert space and conditional expectation. 7. Weak convergence of probability measures. 8. Invariant measures on groups. According to a remark attributed to… Mark Kac probability theory is Measure theory with a Soul. Furthermore measure theory has its own ramifications in topics like function spaces operator theory generalized functions ergodic theory group representations quantum probability etc. On the other hand recent explosive developments in the applications of probability theory have imposed the need for a good grasp of measure theory among a wide spectrum of scholars ranging from economists to engineers and physicists to psychologists. This book with its choice of proofs remarks examples and exercises has been prepared taking both these aesthetic and practical aspects into account. 339 pp.
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Verlag: Oliver & Boyd, 1963
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In den WarenkorbHardcover. Zustand: Good. No Dust Jacket. First Edition. 110 pages. Some wear to the spine and cover edges, lightly toned pages; a solid reading copy. No jacket. Quantity Available: 1. Category: Mathematics; Inventory No: 230488.
Sprache: Englisch
Verlag: Springer London, Limited, 2004
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Hardcover. Octavo, XIII, 427 pages. In Good minus condition. Spine is brown with white print. Boards in brown and white cloth with brown and white print. Wear to spine caps and corners, ink smudge on front, mild shelf wear. Text block has name in ink on title page. 1371029. FP New Rockville Stock.
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Paperback. Zustand: new. Paperback. The evolution of Artificial Intelligence in the 21st century has moved far beyond heuristic techniques and surface-level statistical approximations. Modern AI-whether it is Deep Learning, Reinforcement Learning, Bayesian Modeling, Diffusion Models, or Large Language Models-depends on deep math…ematical principles rooted in measure theory and probability theory. Without understanding the foundational mathematics, practitioners often treat AI models as black boxes, missing the underlying structure that governs learning, generalization, uncertainty, and convergence.This book, Measure Theory and Probability for Artificial Intelligence, written by Anshuman Mishra, is designed to fill this crucial gap. It presents an authoritative, rigorous, yet deeply intuitive exploration of the mathematical foundations that support every modern AI system. This is not just a textbook. It is a complete journey into the mathematical soul of machine intelligence-crafted for ambitious students, dedicated researchers, and serious AI professionals who want to elevate their understanding to the highest level.Where most machine learning books begin directly with algorithms, this book starts from first principles-from sets, sigma-algebras, measurable functions, measures, integrals, probability spaces, convergence modes, inequalities, stochastic processes, and advanced probabilistic tools-then meticulously climbs up to how each of these mathematical structures is used in neural networks, reinforcement learning, generative models, stochastic gradient descent, Bayesian inference, and optimal transport.This makes the book unparalleled in scope and depth. Whether you are an AI engineer trying to understand "why" deep learning works, a PhD researcher developing new models, or a student preparing for advanced coursework, this book offers you the mathematical clarity and conceptual strength required to advance confidently into the technical frontier of artificial intelligence. Why This Book?1. AI is No Longer Only Engineering-It is Mathematical ScienceMachine learning and deep learning operate in high-dimensional spaces, mapping complex random variables through nonlinear transformations. Reinforcement learning optimizes over stochastic returns. Diffusion models rely on stochastic differential equations. Generative models measure the divergence between probability distributions. Neural networks approximate integrals, gradients, and transformations of measures. All of this requires a deep understanding of: Measure theoryProbability theoryInformation theoryStochastic processesMartingale convergenceOptimal transportHigh-dimensional concentrationWithout these tools, AI becomes guesswork.2. Artificial Intelligence Requires RigorThis book is intentionally designed to build mathematical intuition and formal rigor. Every concept is introduced from first principles, but always connected to real AI applications. This dual approach makes the book ideal for: Undergraduate and postgraduate studentsPhD scholarsMachine learning engineersReinforcement learning researchersData scientistsAI hobbyists with a strong mathematical interestFaculty members designing academic courses3. Written in an Accessible, Progressive StyleThe book does not assume a background in measure theory. Instead, it builds the theory step-by-step, starting from sets and functions, gradually rising through: s-algebrasMeasuresIntegrationConvergenceProbability triplesRandom variablesDistribution transformationsExpectation and conditional expecta Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
