A user's guide to measure theoretic probability:
Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2002
|
| Schriftenreihe: | Cambridge series on statistical and probabilistic mathematics
8 |
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xiii, 351 pages) |
| ISBN: | 9780511811555 |
| DOI: | 10.1017/CBO9780511811555 |
Internformat
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| 520 | |a Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean | ||
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Datensatz im Suchindex
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| author | Pollard, David 1950- |
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| author_facet | Pollard, David 1950- |
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| author_sort | Pollard, David 1950- |
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| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9780511811555 |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:14Z |
| institution | BVB |
| isbn | 9780511811555 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029349704 |
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| series2 | Cambridge series on statistical and probabilistic mathematics |
| spelling | Pollard, David 1950- Verfasser (DE-588)110615905 aut A user's guide to measure theoretic probability David Pollard Cambridge Cambridge University Press 2002 1 online resource (xiii, 351 pages) txt rdacontent c rdamedia cr rdacarrier Cambridge series on statistical and probabilistic mathematics 8 Title from publisher's bibliographic system (viewed on 05 Oct 2015) Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean Probabilities Measure theory Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd rswk-swf Maßtheorie (DE-588)4074626-4 gnd rswk-swf Maßtheorie (DE-588)4074626-4 s Wahrscheinlichkeitsrechnung (DE-588)4064324-4 s 1\p DE-604 Erscheint auch als Druckausgabe 978-0-521-00289-9 Erscheint auch als Druckausgabe 978-0-521-80242-0 https://doi.org/10.1017/CBO9780511811555 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Pollard, David 1950- A user's guide to measure theoretic probability Probabilities Measure theory Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Maßtheorie (DE-588)4074626-4 gnd |
| subject_GND | (DE-588)4064324-4 (DE-588)4074626-4 |
| title | A user's guide to measure theoretic probability |
| title_auth | A user's guide to measure theoretic probability |
| title_exact_search | A user's guide to measure theoretic probability |
| title_full | A user's guide to measure theoretic probability David Pollard |
| title_fullStr | A user's guide to measure theoretic probability David Pollard |
| title_full_unstemmed | A user's guide to measure theoretic probability David Pollard |
| title_short | A user's guide to measure theoretic probability |
| title_sort | a user s guide to measure theoretic probability |
| topic | Probabilities Measure theory Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Maßtheorie (DE-588)4074626-4 gnd |
| topic_facet | Probabilities Measure theory Wahrscheinlichkeitsrechnung Maßtheorie |
| url | https://doi.org/10.1017/CBO9780511811555 |
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