Probability measure theory (310 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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Hardcover. Zustand: Very Good. 1996. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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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.
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Sprache: Chinesisch
Verlag: Peking University Press Pub. Date :2004-02-01 (edition ), 1991
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Sprache: Chinesisch
Verlag: Peking University Press Pub. Date :2004-02-01, 1991
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Zustand: Fine. First edition, first printing, 203 pp., Hardcover, fine in a worn dust jacket. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
Sprache: Englisch
Verlag: World Scientific Publishing, 2023
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In den WarenkorbSoft cover. Zustand: New. ISBN:9781944660802,292pp.
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Paperback. Zustand: Very Good. A Basic Course in Measure and Probability: Theory For Applications This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well main…tained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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Sprache: Englisch
Verlag: -, 2012
Serie: Buch 25 von 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Paperback. Zustand: Very Good. Exercises in Probability: A Guided Tour From Measure Theory To Random Processes, Via Conditioning: 35 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 35) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limite…d signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.
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Zustand: Fair. Volume 13. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9780521825…856.
Verlag: Wadsworth,, 1986
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Sprache: Englisch
Verlag: Cambridge University Press, 2012
Serie: Buch 25 von 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Zustand: Fine. 228 pp., hardcover, previous owner's name to front pastedown else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
Sprache: Englisch
Verlag: WSPC, 2022
Serie: Buch 3 von 4 - World Scientific Series on Probability Theory and Its Applications
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Sprache: Englisch
Verlag: World Scientific Publishing Company 4/21/2022, 2022
Serie: Buch 3 von 4 - World Scientific Series on Probability Theory and Its Applications
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Paperback or Softback. Zustand: New. Introduction to Probability Theory: A First Course on the Measure-Theoretic Approach. Book.
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Zustand: New. pp. 528.
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Zustand: Very Good. Very Good condition. 2nd edition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.
Sprache: Englisch
Verlag: WSPC, 2022
Serie: Buch 3 von 4 - World Scientific Series on Probability Theory and Its Applications
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Sprache: Englisch
Verlag: World Scientific Publishing Co Pte Ltd, SG, 2022
Serie: Buch 3 von 4 - World Scientific Series on Probability Theory and Its Applications
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Paperback. Zustand: New. This book provides a first introduction to the methods of probability theory by using the modern and rigorous techniques of measure theory and functional analysis. It is geared for undergraduate students, mainly in mathematics and physics majors, but also for students from other subject areas such as eco…nomics, finance and engineering. It is an invaluable source, either for a parallel use to a related lecture or for its own purpose of learning it.The first part of the book gives a basic introduction to probability theory. It explains the notions of random events and random variables, probability measures, expectation values, distributions, characteristic functions, independence of random variables, as well as different types of convergence and limit theorems. The first part contains two chapters. The first chapter presents combinatorial aspects of probability theory, and the second chapter delves into the actual introduction to probability theory, which contains the modern probability language. The second part is devoted to some more sophisticated methods such as conditional expectations, martingales and Markov chains. These notions will be fairly accessible after reading the first part.
