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Measure-Theoretic Probability : With Applications to Statistics, Finance, and Engineering / by Kenneth Shum
(Compact Textbooks in Mathematics. ISSN:2296455X)
Edition | 1st ed. 2023. |
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Publisher | (Cham : Springer International Publishing : Imprint: Birkhäuser) |
Year | 2023 |
Language | English |
Size | XV, 259 p. 33 illus., 25 illus. in color : online resource |
Authors | *Shum, Kenneth author SpringerLink (Online service) |
Subjects | LCSH:Probabilities LCSH:Measure theory FREE:Probability Theory FREE:Applied Probability FREE:Measure and Integration |
Notes | Preface -- Beyond discrete and continuous random variables -- Probability spaces -- Lebesgue–Stieltjes measures -- Measurable functions and random variables -- Statistical independence -- Lebesgue integral and mathematical expectation -- Properties of Lebesgue integral and convergence theorems -- Product space and coupling -- Moment generating functions and characteristic functions -- Modes of convergence -- Laws of large numbers -- Techniques from Hilbert space theory -- Conditional expectation -- Levy’s continuity theorem and central limit theorem -- References -- Index This textbook offers an approachable introduction to measure-theoretic probability, illustrating core concepts with examples from statistics and engineering. The author presents complex concepts in a succinct manner, making otherwise intimidating material approachable to undergraduates who are not necessarily studying mathematics as their major. Throughout, readers will learn how probability serves as the language in a variety of exciting fields. Specific applications covered include the coupon collector’s problem, Monte Carlo integration in finance, data compression in information theory, and more. Measure-Theoretic Probability is ideal for a one-semester course and will best suit undergraduates studying statistics, data science, financial engineering, and economics who want to understand and apply more advanced ideas from probability to their disciplines. As a concise and rigorous introduction to measure-theoretic probability, it is also suitable for self-study. Prerequisites include a basic knowledge of probability and elementary concepts from real analysis HTTP:URL=https://doi.org/10.1007/978-3-031-49830-5 |
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E-Book | Location | Media type | Volume | Call No. | Status | Reserve | Comments | ISBN | Printed | Restriction | Designated Book | Barcode No. |
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E-Book | オンライン | 電子ブック |
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Springer eBooks | 9783031498305 |
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電子リソース |
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EB00226244 |
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Material Type | E-Book |
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Classification | LCC:QA273.A1-274.9 DC23:519.2 |
ID | 4001106370 |
ISBN | 9783031498305 |