Search results for "Stochastic matrix"
showing 10 items of 20 documents
JOINT TOPOLOGY LEARNING AND GRAPH SIGNAL RECOVERY VIA KALMAN FILTER IN CAUSAL DATA PROCESSES
2018
In this paper, a joint graph-signal recovery approach is investigated when we have a set of noisy graph signals generated based on a causal graph process. By leveraging the Kalman filter framework, a three steps iterative algorithm is utilized to predict and update signal estimation as well as graph topology learning, called Topological Kalman Filter or TKF. Similar to the regular Kalman filter, we first predict the a posterior signal state based on the prior available data and then this prediction is updated and corrected based on the recently arrived measurement. But contrary to the conventional Kalman filter algorithm, we have no information of the transition matrix and hence we relate t…
Bicausative matrices to measure structural change: Are they a good tool?
1999
The causative-matrix method to analyze temporal change assumes that a matrix transforms one Markovian transition matrix into another by a left multiplication of the first matrix; the method is demand-driven when applied to input-output economics. An extension is presented without assuming the demand-driven or supply-driven hypothesis. Starting from two flow matrices X and Y, two diagonal matrices are searched, one premultiplying and the second postmultiplying X, to obtain a result the closer as possible to Y by least squares. The paper proves that the method is deceptive because the diagonal matrices are unidentified and the interpretation of results is unclear. Keywords : Input-Output ; Ch…
Listwise Recommendation Approach with Non-negative Matrix Factorization
2018
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…
Stochastic factorizations, sandwiched simplices and the topology of the space of explanations
2003
We study the space of stochastic factorizations of a stochastic matrix V, motivated by the statistical problem of hidden random variables. We show that this space is homeomorphic to the space of simplices sandwiched between two nested convex polyhedra, and use this geometrical model to gain some insight into its structure and topology. We prove theorems describing its homotopy type, and, in the case where the rank of V is 2, we give a complete description, including bounds on the number of connected components, and examples in which these bounds are attained. We attempt to make the notions of topology accessible and relevant to statisticians.
A perturbation approach for the response of dynamically modified structural systems
1998
The problem of the structural analysis under changes of dynamical parameters is of particular interest. This is due to the fact that often the real structures are different from the predicted ones. In this paper, an unconditionally stable step-by-step procedure, able to evaluate the deterministic response of linear structures with modifications, is presented. The proposed procedure requires the evaluation of the transition matrix, which is the fundamental operator of the step-by-step solution, by means of a perturbation approach. This technique overcomes the difficulties connected with the evaluation of the eigenproperties of the modified structures usually required to obtain the transition…
Higher order statistics of the response of MDOF linear systems excited by linearly parametric white noises and external excitations
1997
The aim of this paper is the evaluation of higher order statistics of the response of linear systems subjected to external excitations and to linearly parametric white noise. The external excitations considered are deterministic or filtered white noise processes. The procedure implies the knowledge of the transition matrix connected to the linear system; this, however, has already been evaluated for obtaining the statistics at single times. The method, which avoids making further integrations for the evaluation of the higher order statistics, is very advantageous from a computational point of view.
Higher order statistics of the response of MDOF linear systems under polynomials of filtered normal white noises
1997
This paper exploits the work presented in the companion paper in order to evaluate the higher order statistics of the response of linear systems excited by polynomials of filtered normal processes. In fact, by means of a variable transformation, the original system is replaced by a linear one excited by external and linearly parametric white noise excitations. The transition matrix of the new enlarged system is obtained simply once the transition matrices of the original system and of the filter are evaluated. The method is then applied in order to evaluate the higher order statistics of the approximate response of nonlinear systems to which the pseudo-force method is applied.
Higher order statistics of the response of linear systems excited by polynomials of filtered Poisson pulses
1999
The higher order statistics of the response of linear systems excited by polynomials of filtered Poisson pulses are evaluated by means of knowledge of the first order statistics and without any further integration. This is made possible by a coordinate transformation which replaces the original system by a quasi-linear one with parametric Poisson delta-correlated input; and, for these systems, a simple relationship between first order and higher order statistics is found in which the transition matrix of the dynamical new system, incremented by the correction terms necessary to apply the Ito calculus, appears.
Nonquenched Isoscalar Spin-M1Excitations insd-Shell Nuclei
2015
Differential cross sections of isoscalar and isovector spin-M1 (0(+)→1(+)) transitions are measured using high-energy-resolution proton inelastic scattering at E(p)=295 MeV on (24)Mg, (28)Si, (32)S, and (36)Ar at 0°-14°. The squared spin-M1 nuclear transition matrix elements are deduced from the measured differential cross sections by applying empirically determined unit cross sections based on the assumption of isospin symmetry. The ratios of the squared nuclear matrix elements accumulated up to E(x)=16 MeV compared to a shell-model prediction are 1.01(9) for isoscalar and 0.61(6) for isovector spin-M1 transitions, respectively. Thus, no quenching is observed for isoscalar spin-M1 transi…
Microscopic description of low-lying two-phonon states: Electromagnetic transitions
2003
Microscopic description of low-lying two-phonon states in even-even nuclei is introduced. The main building blocks are the quasiparticle random-phase approximation (QRPA) phonons. A realistic microscopic nuclear Hamiltonian, based on the Bonn one-boson-exchange potential, is diagonalized in a basis containing one-phonon and two-phonon components, coupled to a given angular momentum and parity. The QRPA equations are directly used in deriving the equations of motion for the two-phonon states. The Pauli principle is taken into account by diagonalizing the metric matrix and discarding the zero-norm states. The electromagnetic transition matrix elements are derived in terms of the metric matrix…