Examples of using Stochastic processes in English and their translations into Greek
{-}
-
Colloquial
-
Official
-
Medicine
-
Ecclesiastic
-
Financial
-
Official/political
-
Computer
numerical optimization, stochastic processes, and machine learning.
generally with the theory and applications of stochastic processes.
how it is modelled, the mathematics of financial derivatives, and stochastic processes are among the finance topics that students can focus upon.
aims at the advanced training of staff in the area of stochastic processes applied to Finance.
is much easier compared to the dynamic and stochastic processes and modeling needed for urban
the theory of Stochastic Processes and its Applications, Probability Theory
power laws are often thought to be signatures of hierarchy or of specific stochastic processes.
applications Numerical mathematics Stochastic processes Mathematics of Finance:
Furthermore, the water inflow into a reservoir is a stochastic process.
Russian mathematician Andrey Markov. A Markov chain is a stochastic process with the Markov property.
the task is to estimate the parameters of the model that describes the stochastic process.
resulting in a solution which is also a stochastic process.
In this view, randomness is the opposite of determinism in a stochastic process.
The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters(for example,
The parametric methodologies accept that the basic stationary stochastic process has a specific structure which can be depicted utilizing few parameters(for instance,
spherons in the continuous stochastic process of their spheral employment.
which is a stochastic process that starts with n vertices and no edges, and at each step adds one new edge chosen uniformly from the set of missing edges.
Analysis of stochastic processes.
Simulation of stochastic processes with applications in financial engineering.
Transmission of stochastic processes through Linear Time-Invariant Systems.