State space models, also termed dynamic models, relate observations yt; t = 1; 2; :::, on a response variable Y to unobserved "states" or "parameters" t, t=1,2,..., by an observation model for yt given t. The states are assumed to follow a Markovian transition model. Gaussian linear state space models are de ned by a linear observation model and a linear Markovian transition equation yt = z 0 t...

We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state variables in a system. To identify the system, we estimate parameters of the model by EM algorithm and determine the dimension of the state variable by BIC.

abstractthe aim of this study was to evaluate the effect of the glass ceiling on women creative ideas working in administrative province units of lorestan state with moderating of their self-concept variable. the population of this cross-correlation, stady was all women employees of administrative province.units of lorestan state. using stratified sampling,and basis of morgan 306 persons select...

the aim of this study was to evaluate the effect of the glass ceiling on women creative ideas working in administrative province units of lorestan state with moderating of their self-concept variable. the population of this cross-correlation study included all women employees of administrative province units of lorestan state. using stratified sampling, and based on morgan table, 306 persons we...

In HMM-based pattern recognition, the structure of the HMM is often predetermined according to some prior knowledge. In the recognition process, we usually make our judgment based on the maximum likelihood of the HMM, without considering the time-varying property of state-level variables, which unfortunately may lead to incorrect results. In this paper, we analyze the property of state-level va...

Abstract: This paper considers Model Predictive Control (MPC) using a Non-Minimal State Space (NMSS) form, in which the state vector consists only of directly measured system variables. Compared to previous research on NMSS/MPC, the new approach alters the state vector with the introduction of an explicit integral-of-error state variable to preserve steady state tracking of the reference input....

We provide several new results on quantum state space, on lattice of subspaces of an infinite dimensional Hilbert space, and on infinite dimensional Hilbert space equations as well as on connections between them. In particular we obtain an n-variable generalized orthoarguesian equation which holds in any infinite dimensional Hilbert space. Then we strengthen Godowski’s result by showing that in...

We construct a 2-variable link polynomial, called WL, for classical links by considering simultaneously the Kauffman state models for the Alexander and for the Jones polynomials. We conjecture that this polynomial is the product of two 1-variable polynomials, one of which is the Alexander polynomial. We refine WL to an ordered set of 3-variable polynomials for those links in 3-space which conta...