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Item specifics
- Condition
- Book Title
- Measure-theoretic Probability : With Applications to Statistics,
- ISBN
- 9783031498329
- Subject Area
- Mathematics
- Publication Name
- Measure-Theoretic Probability : with Applications to Statistics, Finance, and Engineering
- Publisher
- Springer International Publishing A&G
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Mathematical Analysis
- Publication Year
- 2024
- Series
- Compact Textbooks in Mathematics Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Width
- 6.1 in
- Number of Pages
- Xv, 259 Pages
Measure-theoretic Probability : With Applications to Statistics, Finance, and…
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3031498321
ISBN-13
9783031498329
eBay Product ID (ePID)
2338108786
Product Key Features
Number of Pages
Xv, 259 Pages
Publication Name
Measure-Theoretic Probability : with Applications to Statistics, Finance, and Engineering
Language
English
Subject
Probability & Statistics / General, Mathematical Analysis
Publication Year
2024
Type
Textbook
Subject Area
Mathematics
Series
Compact Textbooks in Mathematics Ser.
Format
Trade Paperback
Dimensions
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.2
Table Of Content
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.
Synopsis
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.
LC Classification Number
QA273.A1-274.9
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