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Measure Theory and Probability - Hardcover, by Athreya Krishna B.; - Very Good u
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A book that does not look new and has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections.
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eBay item number:127278123915
Item specifics
- Condition
- Book Title
- Measure Theory and Probability Theory (Springer Texts in Statisti
- ISBN
- 9780387329031
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
038732903X
ISBN-13
9780387329031
eBay Product ID (ePID)
9038290125
Product Key Features
Number of Pages
Xviii, 619 Pages
Publication Name
Measure Theory and Probability Theory
Language
English
Publication Year
2006
Subject
Probability & Statistics / General, Computer Science, Algebra / General, Mathematical Analysis
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Height
0.5 in
Item Weight
83.2 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2006-922767
Reviews
From the reviews:"...There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006"The reader sees not only how measure theory is used to develop probability theory, but also how probability theory is used in applications. … The discourse is delivered in a theorem proof format and thus is better suited for classroom … . The authors prose is generally well thought out … . will make an attractive choice for a two-semester course on measure and probability, or as a second course for students with a semester of measure or probability theory under their belt." (Peter C. Kiessler, Journal of the American Statistical Association, Vol. 102 (479), 2007)"The book is a well written self-contained textbook on measure and probability theory. It consists of 18 chapters. Every chapter contains many well chosen examples and ends with several problems related to the earlier developed theory (some with hints). … At the very end of the book there is an appendix collecting necessary facts from set theory, calculus and metric spaces. The authors suggest a few possibilities on how to use their book." (Kazimierz Musial, Zentralblatt MATH, Vol. 1125 (2), 2008)"The title of the book consists of the names of its two basic parts. The book's third part is comprised of some special topics from probability theory. … The authors suggest using the book in two-semester graduate programs in statistics or a one-semester seminar on special topics. The material of the book is standard … is clear, comprehensive and 'without being intimidating'." (Rimas Norvaiša, Mathematical Reviews, Issue 2007 f)"Probabilists have a special relationship to measure theory. … The style of writing is clear and precise … . Its wide range of topics and results makes Measure Theory and Probability Theory not only a splendid textbook but also a nice addition to any probabilist's reference library. … a researcher in need of a reference work, or just somebody who wants to learn some measure theory to lighten up your life, Measure Theory and Probability Theory is an excellent text that I highly recommend." (Peter Olofsson, SIAM Review, Vol. 49 (3), 2007), From the reviews: "...There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006 "The reader sees not only how measure theory is used to develop probability theory, but also how probability theory is used in applications. ... The discourse is delivered in a theorem proof format and thus is better suited for classroom ... . The authors prose is generally well thought out ... . will make an attractive choice for a two-semester course on measure and probability, or as a second course for students with a semester of measure or probability theory under their belt." (Peter C. Kiessler, Journal of the American Statistical Association, Vol. 102 (479), 2007) "The book is a well written self-contained textbook on measure and probability theory. It consists of 18 chapters. Every chapter contains many well chosen examples and ends with several problems related to the earlier developed theory (some with hints). ... At the very end of the book there is an appendix collecting necessary facts from set theory, calculus and metric spaces. The authors suggest a few possibilities on how to use their book." (Kazimierz Musial, Zentralblatt MATH, Vol. 1125 (2), 2008) "The title of the book consists of the names of its two basic parts. The book's third part is comprised of some special topics from probability theory. ... The authors suggest using the book in two-semester graduate programs in statistics or a one-semester seminar on special topics. The material of the book is standard ... is clear, comprehensive and 'without being intimidating'." (Rimas Norvaisa, Mathematical Reviews, Issue 2007 f) "Probabilistshave a special relationship to measure theory. ... The style of writing is clear and precise ... . Its wide range of topics and results makes Measure Theory and Probability Theory not only a splendid textbook but also a nice addition to any probabilist's reference library. ... a researcher in need of a reference work, or just somebody who wants to learn some measure theory to lighten up your life, Measure Theory and Probability Theory is an excellent text that I highly recommend." (Peter Olofsson, SIAM Review, Vol. 49 (3), 2007), .,."There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006, From the reviews: "...There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006 "The reader sees not only how measure theory is used to develop probability theory, but also how probability theory is used in applications. ... The discourse is delivered in a theorem proof format and thus is better suited for classroom ... . The authors prose is generally well thought out ... . will make an attractive choice for a two-semester course on measure and probability, or as a second course for students with a semester of measure or probability theory under their belt." (Peter C. Kiessler, Journal of the American Statistical Association, Vol. 102 (479), 2007) "The book is a well written self-contained textbook on measure and probability theory. It consists of 18 chapters. Every chapter contains many well chosen examples and ends with several problems related to the earlier developed theory (some with hints). ... At the very end of the book there is an appendix collecting necessary facts from set theory, calculus and metric spaces. The authors suggest a few possibilities on how to use their book." (Kazimierz Musial, Zentralblatt MATH, Vol. 1125 (2), 2008) "The title of the book consists of the names of its two basic parts. The book's third part is comprised of some special topics from probability theory. ... The authors suggest using the book in two-semester graduate programs in statistics or a one-semester seminar on special topics. The material of the book is standard ... is clear, comprehensive and 'without being intimidating'." (Rimas Norvaisa, Mathematical Reviews, Issue 2007 f) "Probabilists have a special relationship to measure theory. ... The style of writing is clear and precise ... . Its wide range of topics and results makes Measure Theory and Probability Theory not only a splendid textbook but also a nice addition to any probabilist's reference library. ... a researcher in need of a reference work, or just somebody who wants to learn some measure theory to lighten up your life, Measure Theory and Probability Theory is an excellent text that I highly recommend." (Peter Olofsson, SIAM Review, Vol. 49 (3), 2007), "...There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
515.42
Table Of Content
Measures and Integration: An Informal Introduction.- Measures.- Integration.- Lp-Spaces.- Differentiation.- Product Measures, Convolutions, and Transforms.- Probability Spaces.- Independence.- Laws of Large Numbers.- Convergence in Distribution.- Characteristic Functions.- Central Limit Theorems.- Conditional Expectation and Conditional Probability.- Discrete Parameter Martingales.- Markov Chains and MCMC.- Stochastic Processes.- Limit Theorems for Dependent Processes.- The Bootstrap.- Branching Processes.
Synopsis
This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the'rstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a ?rst course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p di'erentation theorems of Lebesgue, L -spaces on R, and do general m- sure at the end of the course with one main application to the construction of product measures. This approach does have the pedagogic advantage of seeing one concrete case ?rst before going to the general one. But this also has the disadvantage in making many students' perspective on m- sure theory somewhat narrow. It leads them to think only in terms of the Lebesgue measure on the real line and to believe that measure theory is intimately tied to the topology of the real line. As students of statistics, probability, physics, engineering, economics, and biology know very well, there are mass distributions that are typically nonuniform, and hence it is useful to gain a general perspective. This book attempts to provide that general perspective right from the beginning. The opening chapter gives an informal introduction to measure and integration theory. It shows that the notions of ?-algebra of sets and countable additivity of a set function are dictated by certain very na- ral approximation procedures from practical applications and that they are not just some abstract ideas., This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the?rstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a ?rst course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p di?erentation theorems of Lebesgue, L -spaces on R, and do general m- sure at the end of the course with one main application to the construction of product measures. This approach does have the pedagogic advantage of seeing one concrete case ?rst before going to the general one. But this also has the disadvantage in making many students' perspective on m- sure theory somewhat narrow. It leads them to think only in terms of the Lebesgue measure on the real line and to believe that measure theory is intimately tied to the topology of the real line. As students of statistics, probability, physics, engineering, economics, and biology know very well, there are mass distributions that are typically nonuniform, and hence it is useful to gain a general perspective. This book attempts to provide that general perspective right from the beginning. The opening chapter gives an informal introduction to measure and integration theory. It shows that the notions of ?-algebra of sets and countable additivity of a set function are dictated by certain very na- ral approximation procedures from practical applications and that they are not just some abstract ideas., This is a graduate level textbook on measure theory and probability theory. It presents the main concepts and results in measure theory and probability theory. It further provides heuristic explanations behind the theory to help students see the big picture., This is a graduate level textbook on measure theory and probability theory. It presents the main concepts and results in measure theory and probability theory in a simple and easy-to-understand way. It further provides heuristic explanations behind the theory to help students see the big picture. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. Prerequisites are kept to the minimal level and the book is intended primarily for first year Ph.D. students in mathematics and statistics., This is a graduate level textbook on measure theory and probability theory. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. It is intended primarily for first year Ph.D. students in mathematics and statistics although mathematically advanced students from engineering and economics would also find the book useful. Prerequisites are kept to the minimal level of an understanding of basic real analysis concepts such as limits, continuity, differentiability, Riemann integration, and convergence of sequences and series. A review of this material is included in the appendix. The book starts with an informal introduction that provides some heuristics into the abstract concepts of measure and integration theory, which are then rigorously developed. The first part of the book can be used for a standard real analysis course for both mathematics and statistics Ph.D. students as it provides full coverage of topics such as the construction of Lebesgue-Stieltjes measures on real line and Euclidean spaces, the basic convergence theorems, Lp spaces, signed measures, Radon-Nikodym theorem, Lebesgue's decomposition theorem and the fundamental theorem of Lebesgue integration on R, product spaces and product measures, and Fubini-Tonelli theorems. It also provides an elementary introduction to Banach and Hilbert spaces, convolutions, Fourier series and Fourier and Plancherel transforms. Thus part I would be particularly useful for students in a typical Statistics Ph.D. program if a separate course on real analysis is not astandard requirement. Part II (chapters 6-13) provides full coverage of standard graduate level probability theory. It starts with Kolmogorov's probability model and Kolmogorov's existence theorem. It then treats thoroughly the laws of large numbers including renewal theory and ergodic theorems with applications and then weak convergence of probability distributions, characteristic functions, the Levy-Cramer continuity theorem and the central limit theorem as well as stable laws. It ends with conditional expectations and conditional probability, and an introduction to the theory of discrete time martingales. Part III (chapters 14-18) provides a modest coverage of discrete time Markov chains with countable and general state spaces, MCMC, continuous time discrete space jump Markov processes, Brownian motion, mixing sequences, bootstrap methods, and branching processes. It could be used for a topics/seminar course or as an introduction to stochastic processes. From the reviews: ., ."There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006
LC Classification Number
QA273.A1-274.9
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- c***m (426)- Feedback left by buyer.Past 6 monthsVerified purchaseWOW!; I cannot believe this 4 Days to Hawaii! ; AAA+++; Excellent Service; Great Pricing; Fast Delivery-Faster Than Expected to Hawaii!; Shipped 04/19, Sat, Received 04/24 Thur to Hawaii using free shipping; USPS Ground Mail, Paperback Book in Good Condition--Better Than Described ; TLC Packaging; Excellent Seller Communication, Sends updates . Highly Recommended!, Thank you very much!The Great Crash, 1929 - Paperback, by J K Galbraith - Acceptable (#125958575357)
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- c***m (426)- Feedback left by buyer.Past 6 monthsVerified purchaseWOW!; I cannot believe this 4 Days to Hawaii! ; AAA+++; Excellent Service; Great Pricing; Fast Delivery-Faster Than Expected to Hawaii!; Shipped 04/19, Sat, Received 04/24 Thur to Hawaii using free shipping; USPS Ground Mail, Paperback Book in Good Condition--Better Than Described ; TLC Packaging; Excellent Seller Communication, Sends updates . Highly Recommended!, Thank you very much!The Great Crash, 1929 - Paperback, by J K Galbraith - Acceptable (#125958575357)
- f***f (1600)- Feedback left by buyer.Past 6 monthsVerified purchaseExcellent Seller, Goes the Extra Mile. The Seller Was Incredibly Communicative. Smooth Transaction, Shipped Very Quickly, As Advertised; Good Price; Well Packaged & Delivered Within a Few Days. Item in Described Promised Condition, Thank You Very Much!!!!!!!!!!! A+
- i***9 (8)- Feedback left by buyer.Past monthVerified purchaseI appreciate the deal I was able to get on both books I ordered. They came as pictured and described in the listing and look good! My only complaint is the packaging could be better, I got two paperback books that were not shipped in a box but rather and envelope and one of the covers arrived bent. So I would recommend the seller use bubblewrap or a box to ship future orders.