Generation and analysis of non-Gaussian Markov time series

On simulation of first-order auto-regressive processes with near Laplace marginals

“…In the second direct rejection procedure we find explicit envelope densities tailored to the shape of f ε (x). The rejection strategy is much more flexible than the inverse method used in [3] as it can be extended to other probabilistic models with the minimal knowledge of underlying distributions [7].…”

“…Such singular behaviour is difficult to observe in real-world data sets. In [3], a standard hyperbolic secant density has been used as a model for the marginal density in (1), and the inverse transform method for simulating the resulting time series model has been suggested. However, the proposed formula is rather complex and has a limited scope of applications, since often one can compute the density function but not the corresponding distribution function and its inverse.…”

Asymptotic Performance of Weak Signal Detectors in Multiplicative and First-Order Markov Additive Noise Environment

“…A number of dependent observation models taking into account the dependence among noise components have been proposed and investigated to address such a situation. Among the classes of general dependence model is the first-order Markov model: it has been reported that many signals and noise in telecommunication systems are of this type [2], [3].…”

Analysis of Temporal Symmetry in Non-Gaussian Random Fields

“…Bennett noted that the demon ic non-Gaussian processes that describe physical measuremust return to its o-iginal state to initiate another cycle ments can be produced by both linear and nonlinear modof measurement and work. To return to the original state els [11]. Our interpretation of thermodynamics has not means discarding the just completed measurement; destroyproduced further restrictions on possible random process ing the certan,'y gaiaed by measurement takes work and models for measurements.…”

“…The effects of measurement on a physical system can be the conditional meam [4,11] quantified by considering thermodynamics. The key concept is termodiyamic entropy.…”