Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Valentina Ciccone"'
Publikováno v:
Kybernetika. :740-754
The problem of decomposing a given covariance matrix as the sum of a positive semi-definite matrix of given rank and a positive semi-definite diagonal matrix, is considered. We present a projection-type algorithm to address this problem. This algorit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d23373f844ea6ddb67cc241863b26e61
https://doi.org/10.14736/kyb-2019-4-0740
https://doi.org/10.14736/kyb-2019-4-0740
We consider the problem of steering a linear stochastic system between two end-point degenerate Gaussian distributions in finite time. This accounts for those situations in which some but not all of the state entries are uncertain at the initial, t =
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55f3b827a42570f02ea66c619f27ef2e
http://arxiv.org/abs/2006.10000
http://arxiv.org/abs/2006.10000
Autor:
Valentina Ciccone, Augusto Ferrante
A classical result in spectral estimation establishes that the relative entropy rate between two zero-mean stationary Gaussian processes can be computed explicitly in terms of their spectral densities, hence inducing a pseudo-distance in the cone of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3e66931264f328edaedbab9231da070
http://hdl.handle.net/11577/3365077
http://hdl.handle.net/11577/3365077
We consider the problem of learning dynamic latent variable graphical models. More precisely, given an estimate of the graphical model based on a finite data sample, we propose a new method, which accounts for the uncertainty in the estimation by com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fd14e0777e6f6121579f2d662bb4a19
http://hdl.handle.net/11577/3337150
http://hdl.handle.net/11577/3337150
Publikováno v:
CDC
Motivated by graphical models, we consider the "Sparse Plus Low-rank" decomposition of a positive definite concentration matrix -- the inverse of the covariance matrix. This is a classical problem for which a rich theory and numerical algorithms have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0612d7aaaf0a9fbab8f6ebd87ecd17a0
http://arxiv.org/abs/1901.10613
http://arxiv.org/abs/1901.10613
Factor models are a very efficient way to describe high dimensional vectors of data in terms of a small number of common relevant factors. This problem, which is of fundamental importance in many disciplines, is usually reformulated in mathematical t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bc4978cc3f0429418a2d5f50b11a7c4
http://arxiv.org/abs/1709.01168
http://arxiv.org/abs/1709.01168
Publikováno v:
CDC
Factor analysis aims to describe high dimensional random vectors by means of a small number of unknown common factors. In mathematical terms, it is required to decompose the covariance matrix $\Sigma$ of the random vector as the sum of a diagonal mat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b30c07ecf83cfb754578fb4e2aaafc1a
http://arxiv.org/abs/1708.00401
http://arxiv.org/abs/1708.00401