Sentence examples for based on stochastic differential from inspiring English sources

Multiple optimal stopping problems based on stochastic differential equations (SDEs) are among the ones that have been analyzed most in detail because of their rich mathematical structures [8, 9].

Indeed, aside from Lévy, Brownian or combination thereof, there are also other ways to stochastically model animal movement, such as the Lagrangian approach which is also based on stochastic differential equation (for a nice review, one can refer to [39]).

In this paper, we develop a simple model based on stochastic differential equations, with which we can analytically study the time-evolution of peer-to-peer storage systems under churn, and the interplay between object maintenance and churn.

In this regard Brownian motion of a single microscopic particle is discussed to intuitively introduce the subject and point out the need for a stochastic representation of the phenomena based on stochastic differential equations (SDEs).

We consider two different approaches to evaluate the model output uncertainty, the output error method that lumps all uncertainty into the observation noise term, and a method based on Stochastic Differential Equations (SDEs) that separates input and model structure uncertainty from observation uncertainty and allows updating of model states in real-time.

However, several simplifying assumptions were made in the calculations based on stochastic differential equations.

Our approach to optimal control problems for stochastic delay evolution equations is based on backward stochastic differential equations (BSDEs), which were first introduced by Pardoux and Peng [1]: see [2 4], as general references.

A Monte Carlo simulation approach is presented for solving two problems based on stochastic ordinary differential equations, namely an initial-value problem for a nonlinear ordinary differential equation and a two-point boundary-value problem for a linear equation.

Thus, the suggestion is to represent the GF as a Gaussian Markov Random Field (GMRF) based on Stochastic Partial Differential Equations (SPDE) [ 15].

Mathematics of phylogenetics will grow to foster alternative ways to describe evolution: generalization of trees into nets and developing models based on stochastic and partial differential equations.

NORMA is based on stochastic gradient descent.