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Elsevier

Stochastic Differential Equations and Applications

  • 2 Edición - 30 de diciembre de 2007
  • Última edición
  • Autor: X Mao
  • Idioma: Inglés

This advanced undergraduate and graduate text has now been revised and updated to cover the basic principles and applications of various types of stochastic systems, with much on… Leer más

Descripción

This advanced undergraduate and graduate text has now been revised and updated to cover the basic principles and applications of various types of stochastic systems, with much on theory and applications not previously available in book form. The text is also useful as a reference source for pure and applied mathematicians, statisticians and probabilists, engineers in control and communications, and information scientists, physicists and economists.

Puntos claves

  • Has been revised and updated to cover the basic principles and applications of various types of stochastic systems
  • Useful as a reference source for pure and applied mathematicians, statisticians and probabilists, engineers in control and communications, and information scientists, physicists and economists

De interès para

Students and academics, for use as textbook; pure and applied mathematicians; statisticians and probabilists; engineers; information scientists; physicists; and economists

Índice

  • Dedication
  • Preface to the Second Edition
  • Preface from the 1997 Edition
    • Acknowledgements
  • General Notation
    1. 1: Brownian Motions and Stochastic Integrals
      • 1.1 INTRODUCTION
      • 1.2 BASIC NOTATIONS OF PROBABILITY THEORY
      • 1.3 STOCHASTIC PROCESSES
      • 1.4 BROWNIAN MOTIONS
      • 1.5 STOCHASTIC INTEGRALS
      • 1.6 ITÔ’S FORMULA
      • 1.7 MOMENT INEQUALITIES
      • 1.8 GRONWALL-TYPE INEQUALITIES
    2. 2: Stochastic Differential Equations
      • 2.1 INTRODUCTION
      • 2.2 STOCHASTIC DIFFERENTIAL EQUATIONS
      • 2.3 EXISTENCE AND UNIQUENESS OF SOLUTIONS
      • 2.4 LP-ESTIMATES
      • 2.5 ALMOST SURELY ASYMPTOTIC ESTIMATES
      • 2.6 CARATHEODORY’S APPROXIMATE SOLUTIONS
      • 2.7 EULER–MARUYAMA’S APPROXIMATE SOLUTIONS
      • 2.8 SDE AND PDE: FEYNMAN–KAC’S FORMULA
      • 2.9 THE SOLUTIONS AS MARKOV PROCESSES
    3. 3: Linear Stochastic Differential Equations
      • 3.1 INTRODUCTION
      • 3.2 STOCHASTIC LIOUVILLE’S FORMULA
      • 3.3 THE VARIATION-OF-CONSTANTS FORMULA
      • 3.4 CASE STUDIES
      • 3.5 EXAMPLES
    4. 4: Stability of Stochastic Differential Equations
      • 4.1 INTRODUCTION
      • 4.2 STABILITY IN PROBABILITY
      • 4.3 ALMOST SURE EXPONENTIAL STABILITY
      • 4.4 MOMENT EXPONENTIAL STABILITY
      • 4.5 STOCHASTIC STABILIZATION AND DESTABILIZATION
      • 4.6 FURTHER TOPICS
    5. 5: Stochastic Functional Differential Equations
      • 5.1 INTRODUCTION
      • 5.2 EXISTENCE-AND-UNIQUENESS THEOREMS
      • 5.3 STOCHASTIC DIFFERENTIAL DELAY EQUATIONS
      • 5.4 EXPONENTIAL ESTIMATES
      • 5.5 APPROXIMATE SOLUTIONS
      • 5.6 STABILITY THEORY—RAZUMIKHIN THEOREMS
      • 5.7 STOCHASTIC SELF-STABILIZATION
    6. 6: Stochastic Equations of Neutral Type
      • 6.1 INTRODUCTION
      • 6.2 NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS
      • 6.3 NEUTRAL STOCHASTIC DIFFERENTIAL DELAY EQUATIONS
      • 6.4 MOMENT AND PATHWISE ESTIMATES
      • 6.5 Lp-CONTINUITY
      • 6.6 EXPONENTIAL STABILITY
    7. 7: Backward Stochastic Differential Equations
      • 7.1 INTRODUCTION
      • 7.2 MARTINGALE REPRESENTATION THEOREM
      • 7.3 EQUATIONS WITH LIPSCHITZ COEFFICIENTS
      • 7.4 EQUATIONS WITH NON–LIPSCHITZ COEFFICIENTS
      • 7.5 REGULARITIES
      • 7.6 BSDE AND QUASILINEAR PDE
    8. 8: Stochastic Oscillators
      • 8.1 INTRODUCTION
      • 8.2 THE CAMERON–MARTIN-GIRSANOV THEOREM
      • 8.3 NONLINEAR STOCHASTIC OSCILLATORS
      • 8.4 LINEAR STOCHASTIC OSCILLATORS
      • 8.5 ENERGY BOUNDS
    9. 9: Applications to Economics and Finance
      • 9.1 INTRODUCTION
      • 9.2 STOCHASTIC MODELLING IN ASSET PRICES
      • 9.3 OPTIONS AND THEIR VALUES
      • 9.4 OPTIMAL STOPPING PROBLEMS
      • 9.5 STOCHASTIC GAMES
    10. 10: Stochastic Neural Networks
      • 10.1 INTRODUCTION
      • 10.2 STOCHASTIC NEURAL NETWORKS
      • 10.3 STOCHASTIC NEURAL NETWORKS WITH DELAYS
    11. 11: Stochastic Delay Population Systems
      • 11.1 INTRODUCTION
      • 11.2 NOISE INDEPENDENT OP POPULATION SIZES
      • 11.3 NOISE DEPENDENT ON POPULATION SIZES: PART I
      • 11.4 NOISE DEPENDENT ON POPULATION SIZES: PART II
      • 11.5 STOCHASTIC DELAY LOTKA–VOLTERRA FOOD CHAIN
    12. Bibliographical Notes
    13. References
    14. Index

    Reseñas

    "A helpful book for both experts and beginners in pure and applied mathematics, and in probability theory, systems dynamics, and control theory. An enjoyable read." —(Review of the first edition) Professor Martynuk, Ukraine Academy of Sciences

    "…a welcome and important addition to stochastic differential equations. …giving a clear presentation of the fundamental underpinnings of stochastic differential equations [including the] known theory. …also the development of new results and methods. …both the depth and breadth of the coverage are remarkable."—Professor G.G. Yin, Wayne State University, USA

    Detalles del producto

    • Edición: 2
    • Última edición
    • Publicado: 30 de diciembre de 2007
    • Idioma: Inglés

    Sobre el autor

    XM

    X Mao

    Xuerong Mao, Strathclyde University, UK
    Afiliaciones y experiencia
    Strathclyde University, UK

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