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A basic course in measure and probab...

Cambanis, Stamatis, (1943-1995.)

A basic course in measure and probability :theory for applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A basic course in measure and probability :Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras.
Reminder of title:	theory for applications /
Author:	Leadbetter, M.R.
other author:	Cambanis, Stamatis,
Published:	Cambridge ;Cambridge University Press,2014.
Description:	xiv, 360 p. :ill. ;24 cm.
Subject:	Measure theory.
ISBN:	9781107020405 (hbk.) :

A basic course in measure and probability :theory for applications /
Leadbetter, M.R.

A basic course in measure and probability :theory for applications /Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras. - Cambridge ;Cambridge University Press,2014. - xiv, 360 p. :ill. ;24 cm.

Includes bibliographical references and index.

Machine generated contents note: Preface; Acknowledgements; 1. Point sets and certain classes of sets; 2. Measures: general properties and extension; 3. Measurable functions and transformations; 4. The integral; 5. Absolute continuity and related topics; 6. Convergence of measurable functions, Lp-spaces; 7. Product spaces; 8. Integrating complex functions, Fourier theory and related topics; 9. Foundations of probability; 10. Independence; 11. Convergence and related topics; 12. Characteristic functions and central limit theorems; 13. Conditioning; 14. Martingales; 15. Basic structure of stochastic processes; References; Index.

"Originating from the authors’ own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm ’take-off point’ for them as they specialize in areas that exploit mathematical machinery"--

ISBN: 9781107020405 (hbk.) :NT$3051

LCCN: 2013028841Subjects--Topical Terms:

182047
Measure theory.

LC Class. No.: QC20.7.M43 / L43 2014

Dewey Class. No.: 515/.42

A basic course in measure and probability :theory for applications /
LDR:02327cam a2200217 a 4500 001 425667
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008 140821s2014 enka b 001 0 eng
010 $a 2013028841
020 $a 9781107020405 (hbk.) : $c NT$3051
020 $a 9781107652521 (pbk.)
035 $a 17825660
040 $a DLC $b eng $c DLC $d DLC
042 $a pcc
050 0 0 $a QC20.7.M43 $b L43 2014
082 0 0 $a 515/.42 $2 23
100 1 $a Leadbetter, M.R. $3 669538
245 1 2 $a A basic course in measure and probability : $b theory for applications / $c Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras.
260 $a Cambridge ; $a New York : $b Cambridge University Press, $c 2014.
300 $a xiv, 360 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 8 $a Machine generated contents note: Preface; Acknowledgements; 1. Point sets and certain classes of sets; 2. Measures: general properties and extension; 3. Measurable functions and transformations; 4. The integral; 5. Absolute continuity and related topics; 6. Convergence of measurable functions, Lp-spaces; 7. Product spaces; 8. Integrating complex functions, Fourier theory and related topics; 9. Foundations of probability; 10. Independence; 11. Convergence and related topics; 12. Characteristic functions and central limit theorems; 13. Conditioning; 14. Martingales; 15. Basic structure of stochastic processes; References; Index.
520 $a "Originating from the authors’ own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm ’take-off point’ for them as they specialize in areas that exploit mathematical machinery"-- $c Provided by publisher.
650 0 $a Measure theory. $3 182047
650 0 $a Probabilities. $3 182046
700 1 $a Cambanis, Stamatis, $d 1943-1995. $3 669539
700 1 $a Pipiras, Vladas. $3 669540