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The difficulty of the discovery can be envisaged as being dependent on some measure of dynamic complexity, x, that is described by a process nicefrac{dkappa}{dt}=dot{kappa}=x.
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In the current paper, we consider the case when the shock arrival process is described by a Polya process which has dependent interarrival times.
The phenomenon of missing measurements is described by a Bernoulli process.
Motivated by this, we model an elastic solid with a Young′s modulus distribution described by a Gaussian process.
The nonlinear stochastic systems are represented by the Takagi Sugeno (T S) model and the channel fading is described by a random process.
The intermittent measurements are described by a Bernoulli process and the phenomenon of the partially known transition probabilities is modeled by employing the polytopic uncertainties.
The occurrence times of the inputs are assumed to be described by a Poisson process, while the amounts of the material transferred by the batch units allowed changing according to general probability distributions.
All the random variables in a stochastic system can be described by a stochastic process.
The system is described by a state process X k ;k=0,1,…, where (mathbf {X}_{k} in {0,1}^{d}) is a Boolean vector describing the activation/inactivation state of d genes at time k.
The figure confirms that at the first order the neural network can be described by a normal process, even if small deviations from the normal distribution, due to the non-linearity introduced by (mathscr{A} (V )), can be observed.
That is, deterioration in time of a device is described by a stochastic process ( X ( t ), t ≥ 0 ), in which each X ( t ) represents the degree of deterioration at an instant t.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com