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140122 ||| eng |
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|a 9781461240129
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| 100 |
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|a Simonnet, Michel
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| 245 |
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|a Measures and Probabilities
|h Elektronische Ressource
|c by Michel Simonnet
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| 250 |
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|a 1st ed. 1996
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| 260 |
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|a New York, NY
|b Springer New York
|c 1996, 1996
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| 300 |
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|a 510 p
|b online resource
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| 505 |
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|a I Integration Relative to Daniell Measures -- 1 Riesz Spaces -- 2 Measures on Semirings -- 3 Integrable and Measurable Functions -- 4 Lebesgue Measure on R -- 5 Lp Spaces -- 6 Integrable Functions for Measures on Semirings -- 7 Radon Measures -- 8 Regularity -- II Operations on Measures Defined on Semirings -- 9 Induced Measures and Product Measures -- 10 Radon-Nikodym Derivatives -- 11 Images of Measures -- 12 Change of Variables -- 13 Stieltjes Integral -- 14 The Fourier Transform in Rk -- III Convergence of Random Variables; Conditional Expectation -- 15 The Strong Law of Large Numbers -- 16 The Central Limit Theorem -- 17 Order Statistics -- 18 Conditional Probability -- IV Operations on Radon Measures -- 19 ?-Adequate Family of Measures -- 20 Radon Measures Defined by Densities -- 21 Images of Radon Measures and Product Measures -- 22 Operations on Regular Measures -- 23 Haar Measures -- 24 Convolution of Measures -- Symbol Index
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| 653 |
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|a Probability Theory and Stochastic Processes
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| 653 |
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|a Probabilities
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Universitext
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| 856 |
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|u https://doi.org/10.1007/978-1-4612-4012-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 519.2
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| 520 |
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|a Integration theory holds a prime position, whether in pure mathematics or in various fields of applied mathematics. It plays a central role in analysis; it is the basis of probability theory and provides an indispensable tool in mathe matical physics, in particular in quantum mechanics and statistical mechanics. Therefore, many textbooks devoted to integration theory are already avail able. The present book by Michel Simonnet differs from the previous texts in many respects, and, for that reason, it is to be particularly recommended. When dealing with integration theory, some authors choose, as a starting point, the notion of a measure on a family of subsets of a set; this approach is especially well suited to applications in probability theory. Other authors prefer to start with the notion of Radon measure (a continuous linear func tional on the space of continuous functions with compact support on a locally compact space) because it plays an important role in analysis and prepares for the study of distribution theory. Starting off with the notion of Daniell measure, Mr. Simonnet provides a unified treatment of these two approaches
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