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The state vector of the dynamical system under consideration is a Poisson-driven Markov vector process.
The vector process describing the joint values of position and velocity is Markov.
The response is approximated by a diffusive Markov vector process analyzed by using Itô's stochastic differential equations.
The displacement and velocity response of the system form in that case a Poisson-driven Markov vector process.
First, the two-level split and the SIMD friendly map are designed for utilizing the Vector Process Units on MIC.
Suppose an ℓ-dimensional random vector process with output x that may be influenced by some contextual information c.
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Focus is presently on two-dimensional vector processes, but higher-dimensional extensions are also established.
The additive noise terms and are zero-mean Gaussian vector processes independent across the time index.
An alternative approach to direct application of the out-crossing rate for Gaussian and non-Gaussian vector processes is considered.
This paper develops a hybrid approach of spectral representation and random function for simulating stationary stochastic vector processes.
We review their basic properties and discuss a construction of a class of moving average vector processes driven by multivariate Laplace motion.
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