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Measure, Integral and Probability
Taschenbuch von Peter E. Kopp (u. a.)
Sprache: Englisch

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Beschreibung
Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student. The ideas are developed at an easy pace in a form that is suitable for self-study, with an emphasis on clear explanations and concrete examples rather than abstract theory.

For this second edition, the text has been thoroughly revised and expanded. New features include:

· a substantial new chapter, featuring a constructive proof of the Radon-Nikodym theorem, an analysis of the structure of Lebesgue-Stieltjes measures, the Hahn-Jordan decomposition, and a brief introduction to martingales
· key aspects of financial modelling, including the Black-Scholes formula, discussed briefly from a measure-theoretical perspective to help the reader understand the underlying mathematical framework.

In addition, further exercises and examples are provided to encourage the reader to become directly involved with the material.
Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student. The ideas are developed at an easy pace in a form that is suitable for self-study, with an emphasis on clear explanations and concrete examples rather than abstract theory.

For this second edition, the text has been thoroughly revised and expanded. New features include:

· a substantial new chapter, featuring a constructive proof of the Radon-Nikodym theorem, an analysis of the structure of Lebesgue-Stieltjes measures, the Hahn-Jordan decomposition, and a brief introduction to martingales
· key aspects of financial modelling, including the Black-Scholes formula, discussed briefly from a measure-theoretical perspective to help the reader understand the underlying mathematical framework.

In addition, further exercises and examples are provided to encourage the reader to become directly involved with the material.
Zusammenfassung
Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student. The ideas are developed at an easy pace in a form that is suitable for self-study, with an emphasis on clear explanations and concrete examples rather than abstract theory.

For this second edition, the text has been thoroughly revised and expanded. New features include:

· a substantial new chapter, featuring a constructive proof of the Radon-Nikodym theorem, an analysis of the structure of Lebesgue-Stieltjes measures, the Hahn-Jordan decomposition, and a brief introduction to martingales
· key aspects of financial modeling, discussed briefly from a measure-theoretical perspective to help the reader understand the underlying mathematical framework.
Inhaltsverzeichnis
Content.- 1. Motivation and preliminaries.- 1.1 Notation and basic set theory.- 1.2 The Riemann integral: scope and limitations.- 1.3 Choosing numbers at random.- 2. Measure.- 2.1 Null sets.- 2.2 Outer measure.- 2.3 Lebesgue-measurable sets and Lebesgue measure.- 2.4 Basic properties of Lebesgue measure.- 2.5 Borel sets.- 2.6 Probability.- 2.7 Proofs of propositions.- 3. Measurable functions.- 3.1 The extended real line.- 3.2 Lebesgue-measurable functions.- 3.3 Examples.- 3.4 Properties.- 3.5 Probability.- 3.6 Proofs of propositions.- 4. Integral.- 4.1 Definition of the integral.- 4.2 Monotone convergence theorems.- 4.3 Integrable functions.- 4.4 The dominated convergence theorem.- 4.5 Relation to the Riemann integral.- 4.6 Approximation of measurable functions.- 4.7 Probability.- 4.8 Proofs of propositions.- 5. Spaces of integrable functions.- 5.1 The space L1.- 5.2 The Hilbert space L2.- 5.3 The LP spaces: completeness.- 5.4 Probability.- 5.5 Proofs of propositions.- 6. Product measures.- 6.1 Multi-dimensional Lebesgue measure.- 6.2 Product ?-fields.- 6.3 Construction of the product measure.- 6.4 Fubini's theorem.- 6.5 Probability.- 6.6 Proofs of propositions.- 7. The Radon-Nikodym theorem.- 7.1 Densities and conditioning.- 7.2 The Radon-Nikodym theorem.- 7.3 Lebesgue-Stieltjes measures.- 7.4 Probability.- 7.5 Proofs of propositions.- 8. LimitL theorems.- 8.1 Modes of convergence.- 8.2 Probability.- 8.3 Proofs of propositions.- Solutions.- References.
Details
Erscheinungsjahr: 2004
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 328
Reihe: Springer Undergraduate Mathematics Series
Inhalt: xv
311 S.
3 s/w Illustr.
311 p. 3 illus.
ISBN-13: 9781852337810
ISBN-10: 1852337818
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Kopp, Peter E.
Capinski, Marek
Auflage: 2nd ed. 2004
Hersteller: Springer London
Springer Undergraduate Mathematics Series
Maße: 235 x 178 x 18 mm
Von/Mit: Peter E. Kopp (u. a.)
Erscheinungsdatum: 27.08.2004
Gewicht: 0,573 kg
preigu-id: 102480236
Zusammenfassung
Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student. The ideas are developed at an easy pace in a form that is suitable for self-study, with an emphasis on clear explanations and concrete examples rather than abstract theory.

For this second edition, the text has been thoroughly revised and expanded. New features include:

· a substantial new chapter, featuring a constructive proof of the Radon-Nikodym theorem, an analysis of the structure of Lebesgue-Stieltjes measures, the Hahn-Jordan decomposition, and a brief introduction to martingales
· key aspects of financial modeling, discussed briefly from a measure-theoretical perspective to help the reader understand the underlying mathematical framework.
Inhaltsverzeichnis
Content.- 1. Motivation and preliminaries.- 1.1 Notation and basic set theory.- 1.2 The Riemann integral: scope and limitations.- 1.3 Choosing numbers at random.- 2. Measure.- 2.1 Null sets.- 2.2 Outer measure.- 2.3 Lebesgue-measurable sets and Lebesgue measure.- 2.4 Basic properties of Lebesgue measure.- 2.5 Borel sets.- 2.6 Probability.- 2.7 Proofs of propositions.- 3. Measurable functions.- 3.1 The extended real line.- 3.2 Lebesgue-measurable functions.- 3.3 Examples.- 3.4 Properties.- 3.5 Probability.- 3.6 Proofs of propositions.- 4. Integral.- 4.1 Definition of the integral.- 4.2 Monotone convergence theorems.- 4.3 Integrable functions.- 4.4 The dominated convergence theorem.- 4.5 Relation to the Riemann integral.- 4.6 Approximation of measurable functions.- 4.7 Probability.- 4.8 Proofs of propositions.- 5. Spaces of integrable functions.- 5.1 The space L1.- 5.2 The Hilbert space L2.- 5.3 The LP spaces: completeness.- 5.4 Probability.- 5.5 Proofs of propositions.- 6. Product measures.- 6.1 Multi-dimensional Lebesgue measure.- 6.2 Product ?-fields.- 6.3 Construction of the product measure.- 6.4 Fubini's theorem.- 6.5 Probability.- 6.6 Proofs of propositions.- 7. The Radon-Nikodym theorem.- 7.1 Densities and conditioning.- 7.2 The Radon-Nikodym theorem.- 7.3 Lebesgue-Stieltjes measures.- 7.4 Probability.- 7.5 Proofs of propositions.- 8. LimitL theorems.- 8.1 Modes of convergence.- 8.2 Probability.- 8.3 Proofs of propositions.- Solutions.- References.
Details
Erscheinungsjahr: 2004
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 328
Reihe: Springer Undergraduate Mathematics Series
Inhalt: xv
311 S.
3 s/w Illustr.
311 p. 3 illus.
ISBN-13: 9781852337810
ISBN-10: 1852337818
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Kopp, Peter E.
Capinski, Marek
Auflage: 2nd ed. 2004
Hersteller: Springer London
Springer Undergraduate Mathematics Series
Maße: 235 x 178 x 18 mm
Von/Mit: Peter E. Kopp (u. a.)
Erscheinungsdatum: 27.08.2004
Gewicht: 0,573 kg
preigu-id: 102480236
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