Приклади вживання Stochastic process Англійська мовою та їх переклад на Українською
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Stochastic process.
In these approaches,the task is to estimate the parameters of the model that describes the stochastic process.
The stochastic process of changing the concentration of ammonia nitrogen in the Desna river.
The Wiener process is widely considered the most studied and central stochastic process in probability theory.[1][2][3].
This stochastic process of observed colors doesn't have the Markov property.
If the state space is the integers or natural numbers, then the stochastic process is called a discrete or integer-valued stochastic process. .
A stochastic process St is said to follow a GBM if it satisfies the following stochastic differential equation:.
We present new results concerning the synthesis of optimal control for systems of differenceequations that depend on a semi-Markov or Markov stochastic process.
A discrete-time stochastic process satisfying the Markov property is known as a Markov chain.
For example, when someone says that the"entropy" of the English language is about 1 bit per character,they are actually modeling the English language as a stochastic process and talking about its entropy rate.
An increment is the amount that a stochastic process changes between two index values, often interpreted as two points in time.
If the mean of the increment for any two points in time is equal to the time difference multiplied by some constant μ{\displaystyle\mu}, which is a real number,then the resulting stochastic process is said to have drift μ{\displaystyle\mu}.[84][85][86].
A stochastic process can be classified in different ways, for example, by its state space, its index set, or the dependence among the random variables.
If the state space is n{\displaystyle n}-dimensional Euclidean space, then the stochastic process is called a n{\displaystyle n}-dimensional vector process or n{\displaystyle n}-vector process. .
A stochastic process can have many outcomes, due to its randomness, and a single outcome of a stochastic process is called, among other names, a sample function or realization.
This mathematical space can be the integers, the real line, n{\displaystyle n}-dimensional Euclidean space, the complex plane or other mathematical spaces,which reflects the different values that the stochastic process can take.[ 1][ 5][ 28][ 51][ 56].
The Wiener process is a stochastic process with stationary and independent increments that are normally distributed based on the size of the increments.
While academic science is based on the conventional wisdom of the"Altaic" origin of the Turkic peoples but does not recognize the argument of their European ancestral homeland, the attempts to unravel theethnicity of the Scythians will be a slow and a stochastic process. Only with great difficulty will we ever come to the final truth.
In probability theory, a real valued stochastic process X is called a semimartingale if it can be decomposed as the sum of a local martingale and an adapted finite-variation process. .
Since a stochastic process defined by a Markov chain that is irreducible, aperiodic and positive recurrent has a stationary distribution, the entropy rate is independent of the initial distribution.
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, using an autoregressive or moving average model).
A stochastic process, defined via a separate argument, may be shown(mathematically) to have the Markov property and as a consequence to have the properties that can be deduced from this for all Markov processes. .
In the mathematical theory of stochastic processes, local time is a stochastic process associated with diffusion processes such as Brownian motion, that characterizes the amount of time a particle has spent at a given level.
A stochastic process has the Markov property if the conditional probability distribution of future states of the process(conditional on both past and present states) depends only upon the present state, not on the sequence of events that preceded it.
When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers,then the stochastic process is said to be in discrete time.
The Poisson or the Poisson point process is a stochastic process that has different forms and definitions.[99][100] It can be defined as a counting process, which is a stochastic process that represents the random number of points or events up to some time.
When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers,then the stochastic process is said to be in discrete time.[54][55] If the index set is some interval of the real line, then time is said to be continuous.
If the state space is the real line,then the stochastic process is referred to as a real-valued stochastic process or a process with continuous state space. If the state space is n{\displaystyle n}-dimensional Euclidean space, then the stochastic process is called a n{\displaystyle n}-dimensional vector process or n{\displaystyle n}-vector process.[51][52].