Skip to main content

How to discretize stochastic differential equations

  • II. Seminars
  • Conference paper
  • First Online:
Nonlinear Filtering and Stochastic Control

Part of the book series: Lecture Notes in Mathematics ((LNMCIME,volume 972))

Abstract

For applications, the "Monte-Carlo criterion" and the "trajectorial criterion" (which is quite new) seem to be the most useful criterions to measure the quality of a scheme of discretization of a S.D.E. It is of interest to note that the choice of the optimal scheme should be different wheither one wants to realize approximation of Monte-Carlo type, or one wants to simulate the trajectory of the solution corresponding to a given trajectory of (wt).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 46.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • CLARK-CAMERON A Maximum rate of convergence of Discrete Approximation for S.D.E. Stochastic Differential Systems, Ed. B. Grigelionis, Lecture Notes in Control and Information Sciences, 25, Springer (1980)

    Google Scholar 

  • H. DOSS Liens entre équations différentielles stochastiques et ordinaires. Ann. Inst. H. Poincaré, 13, pp. 99–125 (1977) One can also refer to

    MathSciNet  MATH  Google Scholar 

  • SUSSMANN On the gap between deterministic and stochastic ordinary differential equations, Ann. Prob. 6, pp. 19–41 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  • F. LEGLAND "Estimation de paramètres dans les processus stochastiques, en observation incomplète. Application à un problème de radio-astronomie". Thèse de Docteur-Ingénieur. Université Paris IX, Dauphine (1981)

    Google Scholar 

  • MILSHTEIN-Approximate integration of stochastic differential equations. Theory Probas. Appl., 19, pp. 557–562 (1974)

    MathSciNet  Google Scholar 

  • -A Method of second-order accuracy integration of stochastic differential equations. Theory Probab. Appl., 23, pp. 396–401.

    Google Scholar 

  • TALAY-Résolution trajectorielle et analyse numérique des Equations Différentielles Stochastiques. Publications de Mathématiques Appliquées, Marseille-Toulon (submitted for another publication)

    Google Scholar 

  • -"Analyse Numérique des Equations Différentielles Stochastiques". Thèse de troisième Cycle (to appear).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Sanjoy K. Mitter Antonio Moro

Rights and permissions

Reprints and permissions

Copyright information

© 1982 Spring-Verlag

About this paper

Cite this paper

Talay, D. (1982). How to discretize stochastic differential equations. In: Mitter, S.K., Moro, A. (eds) Nonlinear Filtering and Stochastic Control. Lecture Notes in Mathematics, vol 972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0064866

Download citation

  • DOI: https://doi.org/10.1007/BFb0064866

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-11976-0

  • Online ISBN: 978-3-540-39431-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics