東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Elements of measure and probability

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Elements of measure and probability / by Arup Bose.
作者:	Bose, Arup.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xix, 308 p. :ill., digital ;24 cm.
內容註:	Preliminaries -- Classes of Sets -- Introduction to Measures -- Extension of Measures -- Lebesgue-Stieltjes Measures -- Measurable Functions -- Integral -- Basic Inequalities -- Lp Spaces: Topological Properties -- Product Spaces and Transition Measures -- Random Variables and Vectors -- Moments and Cumulants -- Further Modes of Convergence of Functions -- Independence and Basic Conditional Probability -- 0-1 Laws -- Sums of Independent Random Variables -- Convergence of Finite Measures -- Characteristic Functions -- Central Limit Theorem -- Signed Measure -- Randon-Nikodym Theorem -- Fundamental Theorem of Calculus -- Conditional Expectation.
Contained By:	Springer Nature eBook
標題:	Measure theory. -
電子資源:	https://doi.org/10.1007/978-981-95-2758-8
ISBN:	9789819527588

Elements of measure and probability
Bose, Arup.

Elements of measure and probability [electronic resource] /by Arup Bose. - Singapore :Springer Nature Singapore :2025. - xix, 308 p. :ill., digital ;24 cm. - Texts and readings in mathematics,v. 882366-8725 ;. - Texts and readings in mathematics ;v. 88..

Preliminaries -- Classes of Sets -- Introduction to Measures -- Extension of Measures -- Lebesgue-Stieltjes Measures -- Measurable Functions -- Integral -- Basic Inequalities -- Lp Spaces: Topological Properties -- Product Spaces and Transition Measures -- Random Variables and Vectors -- Moments and Cumulants -- Further Modes of Convergence of Functions -- Independence and Basic Conditional Probability -- 0-1 Laws -- Sums of Independent Random Variables -- Convergence of Finite Measures -- Characteristic Functions -- Central Limit Theorem -- Signed Measure -- Randon-Nikodym Theorem -- Fundamental Theorem of Calculus -- Conditional Expectation.

This book can serve as a first course on measure theory and measure theoretic probability for upper undergraduate and graduate students of mathematics, statistics and probability. Starting from the basics, the measure theory part covers Caratheodory's theorem, Lebesgue-Stieltjes measures, integration theory, Fatou's lemma, dominated convergence theorem, basics of Lp spaces, transition and product measures, Fubini's theorem, construction of the Lebesgue measure in Rd, convergence of finite measures, Jordan-Hahn decomposition of signed measures, Radon-Nikodym theorem and the fundamental theorem of calculus. The material on probability covers standard topics such as Borel-Cantelli lemmas, behaviour of sums of independent random variables, 0-1 laws, weak convergence of probability distributions, in particular via moments and cumulants, and the central limit theorem (via characteristic function, and also via cumulants), and ends with conditional expectation as a natural application of the Radon-Nikodym theorem. A unique feature is the discussion of the relation between moments and cumulants, leading to Isserlis' formula for moments of products of Gaussian variables and a proof of the central limit theorem avoiding the use of characteristic functions. For clarity, the material is divided into 23 (mostly) short chapters. At the appearance of any new concept, adequate exercises are provided to strengthen it. Additional exercises are provided at the end of almost every chapter. A few results have been stated due to their importance, but their proofs do not belong to a first course. A reasonable familiarity with real analysis is needed, especially for the measure theory part. Having a background in basic probability would be helpful, but we do not assume a prior exposure to probability.

ISBN: 9789819527588

Standard No.: 10.1007/978-981-95-2758-8doiSubjects--Topical Terms:

516951
Measure theory.

LC Class. No.: QA312

Dewey Class. No.: 515.42

Elements of measure and probability
LDR:03481nmm a2200337 a 4500 001 2414944
003 DE-He213
005 20251001130603.0
006 m d
007 cr nn 008maaau
008 260205s2025 si s 0 eng d
020 $a 9789819527588 $q (electronic bk.)
020 $a 9789819527571 $q (paper)
024 7 $a 10.1007/978-981-95-2758-8 $2 doi
035 $a 978-981-95-2758-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA312
072 7 $a PBKL $2 bicssc
072 7 $a MAT034000 $2 bisacsh
072 7 $a PBKL $2 thema
082 0 4 $a 515.42 $2 23
090 $a QA312 $b .B743 2025
100 1 $a Bose, Arup. $3 3674426
245 1 0 $a Elements of measure and probability $h [electronic resource] / $c by Arup Bose.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xix, 308 p. : $b ill., digital ; $c 24 cm.
490 1 $a Texts and readings in mathematics, $x 2366-8725 ; $v v. 88
505 0 $a Preliminaries -- Classes of Sets -- Introduction to Measures -- Extension of Measures -- Lebesgue-Stieltjes Measures -- Measurable Functions -- Integral -- Basic Inequalities -- Lp Spaces: Topological Properties -- Product Spaces and Transition Measures -- Random Variables and Vectors -- Moments and Cumulants -- Further Modes of Convergence of Functions -- Independence and Basic Conditional Probability -- 0-1 Laws -- Sums of Independent Random Variables -- Convergence of Finite Measures -- Characteristic Functions -- Central Limit Theorem -- Signed Measure -- Randon-Nikodym Theorem -- Fundamental Theorem of Calculus -- Conditional Expectation.
520 $a This book can serve as a first course on measure theory and measure theoretic probability for upper undergraduate and graduate students of mathematics, statistics and probability. Starting from the basics, the measure theory part covers Caratheodory's theorem, Lebesgue-Stieltjes measures, integration theory, Fatou's lemma, dominated convergence theorem, basics of Lp spaces, transition and product measures, Fubini's theorem, construction of the Lebesgue measure in Rd, convergence of finite measures, Jordan-Hahn decomposition of signed measures, Radon-Nikodym theorem and the fundamental theorem of calculus. The material on probability covers standard topics such as Borel-Cantelli lemmas, behaviour of sums of independent random variables, 0-1 laws, weak convergence of probability distributions, in particular via moments and cumulants, and the central limit theorem (via characteristic function, and also via cumulants), and ends with conditional expectation as a natural application of the Radon-Nikodym theorem. A unique feature is the discussion of the relation between moments and cumulants, leading to Isserlis' formula for moments of products of Gaussian variables and a proof of the central limit theorem avoiding the use of characteristic functions. For clarity, the material is divided into 23 (mostly) short chapters. At the appearance of any new concept, adequate exercises are provided to strengthen it. Additional exercises are provided at the end of almost every chapter. A few results have been stated due to their importance, but their proofs do not belong to a first course. A reasonable familiarity with real analysis is needed, especially for the measure theory part. Having a background in basic probability would be helpful, but we do not assume a prior exposure to probability.
650 0 $a Measure theory. $3 516951
650 0 $a Probabilities. $3 518889
650 1 4 $a Measure and Integration. $3 891263
650 2 4 $a Probability Theory. $3 3538789
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Texts and readings in mathematics ; $v v. 88. $3 3791987
856 4 0 $u https://doi.org/10.1007/978-981-95-2758-8
950 $a Mathematics and Statistics (SpringerNature-11649)