We propose a mixture model for blind source separation and deconvolution with adaptive source densities. Data is modelled as a multivariate locally linear random process. We derive an expression for the asymptotic likelihood of a linear process segment, which allows us to formulate and optimize a mixture model via the EM algorithm. The mixture model is able to represent nonstationary (locally, ...

We are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact, that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. We propose a linear regression model within a Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixtu...

background & aim: esophageal cancer is one of the main common causes of death. the high prevalence of esophageal cancer in northern iran is an important public health problem. the main aim of this paper was to assess the factors affecting survival of  atients with esophageal cancer in neighbor provinces around the caspian sea using weibull mixture cure model and mixture cure model based on a no...

Decomposition of absorption spectra using linear regression has been proposed for calculating concentrations of mixture compounds. The method is based on projecting the observed mixture spectrum onto the linear space generated by the reference spectra that correspond to the individual components comprising the mixture. The computed coefficients are then used as estimates for concentration of th...