Examples of using Stochastic process in English and their translations into Portuguese
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In probability theory, a continuous stochastic process is a type of stochastic process that may be said to be"continuous" as a function of its"time"
The model is developed applying the geometric brownian motion stochastic process with mean reversion(gbm-mr),
solution to the problem, it is necessary to approximate the continuos stochastic process associated to the inflows by a scenario tree.
Galton-Watson process, is a stochastic process with applications in fields like Biology,
However, in many applications the starting point is really the finite-dimensional distributions of the stochastic process.
most of these studies assume that the underlying stochastic process is gaussian.
We state the theorem first for the special case when the underlying stochastic process is a Wiener process. .
These figures were obtained from IBGE. The central coordinates of the localities were utilized for estimating the risk, by means of a spatial stochastic process of Gaussian, stationary,
The central coordinates of the localities were utilized for estimating the risk, by means of a spatial stochastic process of Gaussian, stationary,
In applied probability, a regenerative process is a class of stochastic process with the property that certain portions of the process can be treated as being statistically independent of each other.
Suppose that Xt is a real-valued stochastic process defined on a probability space( Ω, F, P){\displaystyle(\Omega,{\mathcal{F}},\mathbb{P})} and with time index t ranging
is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion(also called a Wiener process) with drift.
In mathematics, a Feller-continuous process is a continuous-time stochastic process for which the expected value of suitable statistics of the process at a given time in the future depend continuously on the initial condition of the process. .
The operational research, in the stochastic process area, has greatly contributed for the development of methods
so it is a discrete-time stochastic process that takes only two values,
is sufficient to describe the observed stochastic process.
to decide if the variations explained are sufficient to justify the use of the stochastic process forecast.
to analyze the first chapman compatibility condition of the stochastic process associated to a continuous random variable.
Definitions==A basic definition of a discrete-time martingale is a discrete-time stochastic process(i.e., a sequence of random variables)"X"1,"X"2,"X"3,… that satisfies for any time"n",
The stochastic process defined in extended kalman filter,