L. C. G. Rogers, D. Williams, David Williams
Diffusions, Markov Processes and Martingales
Volume 2, Ito Calculus
L. C. G. Rogers, D. Williams, David Williams
Diffusions, Markov Processes and Martingales
Volume 2, Ito Calculus
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Now available in paperback for the first time; essential reading for all students of probability theory.
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Now available in paperback for the first time; essential reading for all students of probability theory.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- 2nd edition
- Seitenzahl: 496
- Erscheinungstermin: 18. Juni 2014
- Englisch
- Abmessung: 229mm x 152mm x 27mm
- Gewicht: 711g
- ISBN-13: 9780521775939
- ISBN-10: 0521775930
- Artikelnr.: 21545011
- Verlag: Cambridge University Press
- 2nd edition
- Seitenzahl: 496
- Erscheinungstermin: 18. Juni 2014
- Englisch
- Abmessung: 229mm x 152mm x 27mm
- Gewicht: 711g
- ISBN-13: 9780521775939
- ISBN-10: 0521775930
- Artikelnr.: 21545011
Some frequently used notation
4. Introduction to Ito calculus
4.1. Some motivating remarks
4.2. Some fundamental ideas: previsible processes, localization, etc.
4.3. The elementary theory of finite-variation processes
4.4. Stochastic integrals: the L2 theory
4.5. Stochastic integrals with respect to continuous semimartingales
4.6. Applications of Ito's formula
5. Stochastic differential equations and diffusions
5.1. Introduction
5.2. Pathwise uniqueness, strong SDEs, flows
5.3. Weak solutions, uniqueness in law
5.4. Martingale problems, Markov property
5.5. Overture to stochastic differential geometry
5.6. One-dimensional SDEs
5.7. One-dimensional diffusions
6. The general theory
6.1. Orientation
6.2. Debut and section theorems
6.3. Optional projections and filtering
6.4. Characterising previsible times
6.5. Dual previsible projections
6.6. The Meyer decomposition theorem
6.7. Stochastic integration: the general case
6.8. Ito excursion theory
References
Index.
4. Introduction to Ito calculus
4.1. Some motivating remarks
4.2. Some fundamental ideas: previsible processes, localization, etc.
4.3. The elementary theory of finite-variation processes
4.4. Stochastic integrals: the L2 theory
4.5. Stochastic integrals with respect to continuous semimartingales
4.6. Applications of Ito's formula
5. Stochastic differential equations and diffusions
5.1. Introduction
5.2. Pathwise uniqueness, strong SDEs, flows
5.3. Weak solutions, uniqueness in law
5.4. Martingale problems, Markov property
5.5. Overture to stochastic differential geometry
5.6. One-dimensional SDEs
5.7. One-dimensional diffusions
6. The general theory
6.1. Orientation
6.2. Debut and section theorems
6.3. Optional projections and filtering
6.4. Characterising previsible times
6.5. Dual previsible projections
6.6. The Meyer decomposition theorem
6.7. Stochastic integration: the general case
6.8. Ito excursion theory
References
Index.
Some frequently used notation
4. Introduction to Ito calculus
4.1. Some motivating remarks
4.2. Some fundamental ideas: previsible processes, localization, etc.
4.3. The elementary theory of finite-variation processes
4.4. Stochastic integrals: the L2 theory
4.5. Stochastic integrals with respect to continuous semimartingales
4.6. Applications of Ito's formula
5. Stochastic differential equations and diffusions
5.1. Introduction
5.2. Pathwise uniqueness, strong SDEs, flows
5.3. Weak solutions, uniqueness in law
5.4. Martingale problems, Markov property
5.5. Overture to stochastic differential geometry
5.6. One-dimensional SDEs
5.7. One-dimensional diffusions
6. The general theory
6.1. Orientation
6.2. Debut and section theorems
6.3. Optional projections and filtering
6.4. Characterising previsible times
6.5. Dual previsible projections
6.6. The Meyer decomposition theorem
6.7. Stochastic integration: the general case
6.8. Ito excursion theory
References
Index.
4. Introduction to Ito calculus
4.1. Some motivating remarks
4.2. Some fundamental ideas: previsible processes, localization, etc.
4.3. The elementary theory of finite-variation processes
4.4. Stochastic integrals: the L2 theory
4.5. Stochastic integrals with respect to continuous semimartingales
4.6. Applications of Ito's formula
5. Stochastic differential equations and diffusions
5.1. Introduction
5.2. Pathwise uniqueness, strong SDEs, flows
5.3. Weak solutions, uniqueness in law
5.4. Martingale problems, Markov property
5.5. Overture to stochastic differential geometry
5.6. One-dimensional SDEs
5.7. One-dimensional diffusions
6. The general theory
6.1. Orientation
6.2. Debut and section theorems
6.3. Optional projections and filtering
6.4. Characterising previsible times
6.5. Dual previsible projections
6.6. The Meyer decomposition theorem
6.7. Stochastic integration: the general case
6.8. Ito excursion theory
References
Index.