Solving approximately a prediction problem for stochastic jump-diffusion systems

T. A. Averina, K. A. Rybakov

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, a new approach to solving a prediction problem for nonlinear stochastic differential systems with a Poisson component is discussed. In this approach, the prediction problem is reduced to an analysis of stochastic jump-diffusion systems with terminating and branching paths. The prediction problem can be approximately solved by using numerical methods for stochastic differential equations and methods for modeling inhomogeneous Poisson flows.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalNumerical Analysis and Applications
Issue number1
Publication statusPublished - 1 Jan 2017


  • branching processes
  • conditional density
  • Duncan–Mortensen–Zakai equation
  • Kolmogorov–Feller equation
  • Monte Carlo method
  • optimal filtering problem
  • prediction problem
  • stochastic jump-diffusion system
  • Duncan-Mortensen-Zakai equation
  • Kolmogorov-Feller equation


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