Zobrazeno 1 - 10
of 249
pro vyhledávání: '"Lopes, S. R."'
We consider the problem of testing for two Gibbs probabilities $\mu_0$ and $\mu_1$ defined for a dynamical system $(\Omega,T)$. Due to the fact that in general full orbits are not observable or computable, one needs to restrict to subclasses of tests
Externí odkaz:
http://arxiv.org/abs/2112.00670
In this work, we study the class of stochastic process that generalizes the Ornstein-Uhlenbeck processes, hereafter called by \emph{Generalized Ornstein-Uhlenbeck Type Process} and denoted by GOU type process. We consider them driven by the class of
Externí odkaz:
http://arxiv.org/abs/2108.06374
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to
Externí odkaz:
http://arxiv.org/abs/2107.02860
We investigate large deviations properties for centered stationary AR(1) and MA(1) processes with independent Gaussian innovations, by giving the explicit bivariate rate functions for the sequence of random vectors $(\boldsymbol{S}_n)_{n \in \N} = \l
Externí odkaz:
http://arxiv.org/abs/2102.09637
Autor:
Feltes, G. L., Lopes, S. R. C.
Long memory processes driven by L\'evy noise with finite second-order moments have been well studied in the literature. They form a very rich class of processes presenting an autocovariance function which decays like a power function. Here, we study
Externí odkaz:
http://arxiv.org/abs/2011.06067
We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patte
Externí odkaz:
http://arxiv.org/abs/2006.03643
Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences o
Externí odkaz:
http://arxiv.org/abs/2005.03430
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The growing study of time series, especially those related to nonlinear systems, has challenged the methodologies to characterize and classify dynamical structures of a signal. Here we conceive a new diagnostic tool for time series based on the conce
Externí odkaz:
http://arxiv.org/abs/1707.00944