Sentence examples for a sequence of stochastic from inspiring English sources
A sequence of stochastic and independent variables, which obey the Bernoulli distribution, are introduced to depict stochastic time-varying delays.
In a similar way, let us construct a sequence of stochastic processes { X n ( t ), n = 1, 2, … }.
A sequence of stochastic variables is introduced to govern the random occurrences of the nonlinearities, discrete time-delays and distributed time-delays, where all the stochastic variables are mutually independent but obey the Bernoulli distribution.
In this paper, we propose an iterative semi-stochastic algorithm, involving a sequence of stochastic extractions, which considerably decreases the computational cost of the population generation.
Consequently, ({Z^{n}_{4}} _{n=0}^{infty}) is a sequence of fuzzy stochastic process.
Stability of overall closed-loop system is rigorously proved by using the new convergence properties of a sequence of row stochastic matrices.
This pattern suggests that the overall framework for the gating mechanism is that of an approximately linear sequence of stochastic domain motions (a 'Brownian conformational wave') that connects structural changes that regulate transmitter affinity with those that regulate conductance [7].
The software uses an empirical approach to allocate crops to fields (polygons in vector format) over a sequence of years, using a stochastic process (Markov chains) and rule-based constraints.
This is consistent with the direction of guidance being determined by the sequence of stochastic fluctuations of UNC-40-mediated axon outgrowth activity, rather than by the direction of UNC-40 localization and axon outgrowth at any discrete time.
A discrete-time martingale is a stochastic process where a sequence of random variables has conditional expected value of the next observation equal to the last observation, where.
Presently, the aim is not to derive moment equations, i.e., a deterministic set of equations that approximate the moments of the Markovian particle model, but rather processes (deterministic or stochastic) to which a sequence of microscopic models converges under suitable conditions in a probabilistic way.
