Noun:

స్థిరమైన యాదృచ్ఛిక ప్రక్రియ,

stationary stochastic process's Usage Examples:

distribution of the stationary stochastic process remains the same.

Consider a p-dimensional zero mean stationary stochastic process X ( t ) [ X ( 1 , t ) , X ( 2 , t ) , … , X ( p , t ) ] T.

following Fourier-type decomposition for a continuous time stationary stochastic process: there exists a stochastic process ω ξ {\displaystyle \omega.

The Ornstein isomorphism theorem states that every stationary stochastic process is equivalent to a Bernoulli scheme (a Bernoulli process with.

inhomogeneous (or non-homogeneous) Poisson process, which is a non-stationary stochastic process with a location-dependent density λ(x) where x is a point (usually.

of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two.

" Write Du for the number of times the ergodic stationary stochastic process x(t) takes the value u in a unit of time (i.

homogeneous Poisson point processes, which is an example of a stationary stochastic process.

The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small.

For general stochastic processes strict-sense stationarity implies wide-sense stationarity but not every wide-sense stationary stochastic process.

merely the entropy of each symbol, while, in the case of a stationary stochastic process, it is r lim n → ∞ H ( X n | X n − 1 , X n − 2 , X n − 3 .

isomorphism theorem is even a bit stronger: it states that any stationary stochastic process is isomorphic to a Bernoulli scheme; the Markov chain is just.

(ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR).

Synonyms:

random,

Antonyms:

nonrandom, purposive,