Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
by
Sun, Qiyu
, Tafti, Pouya D
, Unser, Michael
in
Decoupling
/ Differential equations
/ Finite difference method
/ Finite differences
/ Gaussian process
/ Innovations
/ Operators (mathematics)
/ Stochastic models
/ Stochastic processes
/ White noise
2012
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
by
Sun, Qiyu
, Tafti, Pouya D
, Unser, Michael
in
Decoupling
/ Differential equations
/ Finite difference method
/ Finite differences
/ Gaussian process
/ Innovations
/ Operators (mathematics)
/ Stochastic models
/ Stochastic processes
/ White noise
2012
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
by
Sun, Qiyu
, Tafti, Pouya D
, Unser, Michael
in
Decoupling
/ Differential equations
/ Finite difference method
/ Finite differences
/ Gaussian process
/ Innovations
/ Operators (mathematics)
/ Stochastic models
/ Stochastic processes
/ White noise
2012
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
Paper
A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
2012
Request Book From Autostore
and Choose the Collection Method
Overview
We introduce a general distributional framework that results in a unifying description and characterization of a rich variety of continuous-time stochastic processes. The cornerstone of our approach is an innovation model that is driven by some generalized white noise process, which may be Gaussian or not (e.g., Laplace, impulsive Poisson or alpha stable). This allows for a conceptual decoupling between the correlation properties of the process, which are imposed by the whitening operator L, and its sparsity pattern which is determined by the type of noise excitation. The latter is fully specified by a Levy measure. We show that the range of admissible innovation behavior varies between the purely Gaussian and super-sparse extremes. We prove that the corresponding generalized stochastic processes are well-defined mathematically provided that the (adjoint) inverse of the whitening operator satisfies some Lp bound for p>=1. We present a novel operator-based method that yields an explicit characterization of all Levy-driven processes that are solutions of constant-coefficient stochastic differential equations. When the underlying system is stable, we recover the family of stationary CARMA processes, including the Gaussian ones. The approach remains valid when the system is unstable and leads to the identification of potentially useful generalizations of the Levy processes, which are sparse and non-stationary. Finally, we show how we can apply finite difference operators to obtain a stationary characterization of these processes that is maximally decoupled and stable, irrespective of the location of the poles in the complex plane.
Publisher
Cornell University Library, arXiv.org
This website uses cookies to ensure you get the best experience on our website.