References

  1. E. Allen (2007), "Modeling with Itô Stochastic Differential Equations", Mathematical Modelling: Theory and Applications, Vol. 22, Springer Netherlands. DOI: 10.1007/978-1-4020-5953-7

  2. Y. Asai (2016), "Numerical Methods for Random Ordinary Differential Equations and their Applications in Biology and Medicine", tese de doutorado, Johann Wolfgang Goethe-Universität.

  3. R. Bass (2011), "Stochastic Processes", Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press.

  4. M. H. DeGroot, M. J. Schervish (2019), "Probability and statistics", 4th Edition, Pearson Education, Inc.

  5. B. Eisenberg & R. Sullivan (2008), "Why Is the Sum of Independent Normal Random Variables Normal?", Mathematics Magazine, vol. 81, no. 5, 362-366. DOI: 10.1080/0025570X.2008.11953577

  6. L. C. Evans (2013), "An Introduction to Stochastic Differential Equations", American Mathematical Societiy.

  7. H. Fischer (2011), "A History of the Central Limit Theorem. From Classical to Modern Probability Theory", Sources and Studies in the History of Mathematics and Physical Sciences,Springer, New York, NY. DOI: 10.1007/978-0-387-87857-7

  8. X. Han & P. E. Kloeden (2017), "Random Ordinary Differential Equations and Their Numerical Solution", Probability Theory and Stochastic Modelling, vol. 85, Springer Singapore. DOI: 10.1007/978-981-10-6265-0

  9. D. J. Higham & P. E. Kloeden (2021), "An Introduction to the Numerical Simulation of Stochastic Differential Equations", SIAM.

  10. E. T. Jaynes (2003), "Probability Theory: The Logic of Science." Cambridge Univerity Press, Cambridge. DOI: 10.1017/CBO9780511790423

  11. Y. Komori, Y. Saito, T. Mitsui (1994), Some issues in discrete approximate solution for stochastic differential equations, Computers & Mathematics with Applications, Volume 28, Issues 10–12, 269-278

  12. P. E. Kloeden & E. Platen (1992), "Numerical Solution of Stochastic Differential Equations", Springer-Verlag. DOI: 10.1007/978-3-662-12616-5

  13. D. E. Knuth (1997), "The Art of Computer Programming II: Seminumerical Algorithms", Addison-Wesley Longman Publishing Co., Inc.

  14. P. Mörters & Y. Peres (2010), "Brownian motion", Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge. With an appendix by Oded Schramm and Wendelin Werner

  15. D. Mumford (2000), The Dawning of the Age of Stochasticity, Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni, Volume: 11, Issue: S1, page 107-125

  16. T. Neckel & F. Rupp (2013), "Random Differential Equations in Scientific Computing", De Gruyter Open Poland. DOI: 10.2478/9788376560267

  17. B. Øksendal (2011), "Stochastic Differential Equations. An Introduction with Applications", Universitext Series, Springer-Verlag Berlin Heidelberg. DOI: 10.1007/978-3-642-14394-6

  18. M. A. Pinsky & S. Karlin (2011), "An Introduction to Stochastic Modeling", Fourth Edition, Elsevier Inc.

  19. Z. Zhang & G. E. Karniadakis (2017), "Numerical Methods for Stochastic Partial Differential Equations with White Noise", Applied Mathematical Sciences, vol. 196, Springer International Publishing. DOI: 10.1007/978-3-319-57511-7



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