«stationary stochastic processes»
۴۱۲۰ نتیجهStatistical inverse problems for non-gaussian non-stationary stochastic processes defined by a set of realizations
This paper presents a method to analyze the transitory response of complex and nonlinear systems, which are excited by non-Gaussian and non-stationary random fields, by solving of a statistical inverse problem with experimental measurements. Based on a double expansion, it is par
Learning Theory Estimates with Observations from General Stationary Stochastic Processes
This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generali
Building a Stationary Stochastic Process from a Finite-dimensional Marginal
If A is a finite alphabet, U ⊂ Z, and μU is a probability measure on A that “looks like” the marginal projection of a stationary stochastic process on A D , then can we “extend” μU to such a process? Under what conditions can we make this extension ergodic, (quasi)periodic, or (w
Orthogonal Rational Functions and Non-Stationary Stochastic Processes: a Szegö Theory
We present a generalization of Szegö theory of orthogonal polynomials on the unit circle to orthogonal rational functions. Unlike previous results, the poles of the rational functions may tend to the unit circle under smoothness assumptions on the density of the measure. Just lik
Linear Estimation of Stationary Stochastic Processes , Vibrating Strings , and Inverse Scattering
Some vibrating string equations are derived for estimating a stationary stochastic process given some observations of this process over a finite interval. These equations are the time-domain counterpart of equations introduced by Krein, and Dym and McKean in the frequency domain.
Stochastic Processes ( Fall 2014 ) Spectral representations and ergodic theorems for stationary stochastic processes Stationary stochastic processes
A stochastic process X is strongly stationary if its fdds are invariant under time shifts, that is, for any (finite) n, for any t0 and for all t1, ..., tn ∈ T , (Xt1 , ..., Xtn) and (Xt1+t0 , ..., Xtn+t0) have the same distribution. A stochastic process X is weakly stationary if
Parametric Estimation of Stationary Stochastic Processes under Indirect Observability
For many natural turbulent dynamic systems, observed high dimensional dynamic data can be approximated at slow time scales by a process Xt driven by a systems of stochastic differential equations (SDEs). When one tries to estimate the parameters of this unobservable SDEs systems,
Reducing non-stationary stochastic processes to stationarity by a time deformation
A necessary and suucient condition is given to reduce a non-stationary random process fZ(t) : t 2 T Rg to stationarity via a bijective diieren-tiable time deformation so that its correlation function r(t; t 0) depends only on the diierence (t 0)?(t) through a stationary correlati
Digital simulation of an arbitrary stationary stochastic process by spectral representation.
In this paper we present a straightforward, efficient, and computationally fast method for creating a large number of discrete samples with an arbitrary given probability density function and a specified spectral content. The method relies on initially transforming a white noise
Building a Stationary Stochastic Process from aFinite - dimensional
If A is a nite alphabet, U Z D , and U is a probability measure on A U that \looks like" the marginal projection of a stationary stochas-tic process on A Z D , then can we \extend" U to such a process? Under what conditions can we make this extension ergodic, (quasi)periodic, or