stochastic process

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Related to stochastic process: Markov chain

sto·chas·tic pro·cess

a process that incorporates some element of randomness.
[G. stochastikos, pertaining to guessing, fr. stochazomai, to guess]
References in periodicals archive ?
Liptser, "On diffusion approximation with discontinuous coefficients," Stochastic Processes and Their Applications, vol.
where B is the scalar standard Brownian motion and [zeta], is an arbitrary predictable stochastic process on [0, [infinity]).
A stochastic process is called discrete-state if [X.sub.t](e) is a discrete random variable for all t [member of] T and continuous-state if [X.sub.t](e) is a continuous random variable, for all t [member of] T.
Tabata, "A stochastic linear-quadratic problem with Levy processes and its application to finance," Stochastic Processes and their Applications, vol.
A fuzzy stochastic process x : [a, b] x [OMEGA] [right arrow] [E.sup.d] is called continuous if there exists [[OMEGA].sub.0] [subset] [OMEGA] with P([[OMEGA].sub.0]) = 1 and such that, for every [omega] [member of] [[OMEGA].sub.0], the trajectory x(*,[omega]) is a continuous function on [a, b] with respect to the metric D.
where [F.sub.x]([omega]) is a complex stationary stochastic process with an orthogonal increment and A([omega], t) is a slowly varying modulation function.
Let F : I x J x [OMEGA] [right arrow] [K.sup.b.sub.c] ([R.sup.d]) be a predictable and [L.sup.2,d.sub.P] ([v.sub.A])- integrally bounded set-valued stochastic process. Then the correspondence
A [A.sub.2](0)-generalized stochastic process on a probability space ([OMEGA], [SIGMA], [mu]) is a map U : [OMEGA] [right arrow] [A.sub.2](0) such that there is a representing function
Stochastic Processes. Science Paperbacks and Methuen and Co.
This paper is organized as follows: after this introduction, we present the theoretical framework with an overview of the Brazilian livestock sector and Real Options literature reviews and modeling uncertainties for Stochastic Processes. Section 3 shows the model used.
In the first case, assuming that the moments of the resistor current are known and applying expected value operator to (1) result in closed form formulae expressing the first and the second moments of the voltage stochastic process across the resistor:
with [delta]B(t) term expressing stochastic processes, and equal to

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