Measure-Theoretic Probability With Applications to Statistics, Finance, and Engineering /
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 n...
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| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Cham :
Springer International Publishing : Imprint: Birkhäuser,
2023.
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| 版: | 1st ed. 2023. |
| シリーズ: | Compact Textbooks in Mathematics,
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-3-031-49830-5 |
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| 要約: | 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. |
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| 物理的記述: | 1 online resource (XV, 259 p. 33 illus.). 25 illus. in color. |
| ISBN: | 9783031498305 |
| ISSN: | 2296-455X |
| DOI: | 10.1007/978-3-031-49830-5 |