A novel representation of functions, called generalized Taylor form, is applied to the filtering of white noise processes. It is shown that every Gaussian colored noise can be expressed as the output of a set of linear fractional stochastic differential equations whose solution is a weighted sum of fractional Brownian motions. The exact form of the weighting coefficients is given and it is shown that it is related to the fractional moments of the target spectral density of the colored noise
Brownian motion can be characterized as a generalized random process and, as such, has a generalized...
Integral equations for the mean-square estimate are obtained for the linear filtering problem, in wh...
Abstract: A fractional Gaussian noise is used in a stochastic differential equation in a Hilbert spa...
A novel representation of functions, called generalized Taylor form, is applied to the filtering of ...
A novel representation of functions, called generalized Taylor form, is applied to the filtering of ...
AbstractIn this paper, a class of Gaussian processes, having locally the same fractal properties as ...
AbstractMaruyama introduced the notation db(t)=w(t)(dt)1/2 where w(t) is a zero-mean Gaussian white ...
An elementary approach is used to derive a Bayes-type formula, extending the Kallianpur--Striebel fo...
Using the white noise space framework, we construct and study a class of Gaussian processes with sta...
Abstract. An elementary approach is used to derive a Bayes type formula, extend-ing the Kallianpur-S...
Fractional Brownian motion (FBM) with Hurst parameter index between 0 and 1 is a stochastic process ...
AbstractWe construct fractional Brownian motion, sub-fractional Brownian motion and negative sub-fra...
Stochastic integration with respect to Gaussian processes, such as fractional Brownian motion (fBm) ...
AbstractUsing the white noise space framework, we construct and study a class of Gaussian processes ...
We present a white noise calculus for d-parameter fractional Brownian motion B-H (x, omega); x is an...
Brownian motion can be characterized as a generalized random process and, as such, has a generalized...
Integral equations for the mean-square estimate are obtained for the linear filtering problem, in wh...
Abstract: A fractional Gaussian noise is used in a stochastic differential equation in a Hilbert spa...
A novel representation of functions, called generalized Taylor form, is applied to the filtering of ...
A novel representation of functions, called generalized Taylor form, is applied to the filtering of ...
AbstractIn this paper, a class of Gaussian processes, having locally the same fractal properties as ...
AbstractMaruyama introduced the notation db(t)=w(t)(dt)1/2 where w(t) is a zero-mean Gaussian white ...
An elementary approach is used to derive a Bayes-type formula, extending the Kallianpur--Striebel fo...
Using the white noise space framework, we construct and study a class of Gaussian processes with sta...
Abstract. An elementary approach is used to derive a Bayes type formula, extend-ing the Kallianpur-S...
Fractional Brownian motion (FBM) with Hurst parameter index between 0 and 1 is a stochastic process ...
AbstractWe construct fractional Brownian motion, sub-fractional Brownian motion and negative sub-fra...
Stochastic integration with respect to Gaussian processes, such as fractional Brownian motion (fBm) ...
AbstractUsing the white noise space framework, we construct and study a class of Gaussian processes ...
We present a white noise calculus for d-parameter fractional Brownian motion B-H (x, omega); x is an...
Brownian motion can be characterized as a generalized random process and, as such, has a generalized...
Integral equations for the mean-square estimate are obtained for the linear filtering problem, in wh...
Abstract: A fractional Gaussian noise is used in a stochastic differential equation in a Hilbert spa...