in this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. then a common general forward modeling for them is given and the corresponding inversion problem is presented. then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a baye...

objectives : schizophrenia patients often experience relapses once and even more with no limit on number of relapses. the time among relapses are rarely considered in studies. the aim of this study was to identify some risk factors of time to elapses in schizophrenic patients with recurrent events model in survival analysis. methods : in a retrospective longitudinal study, the medical records o...

A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal distribution. Specific distributions examined include the normal, student-t, slash and the variance gamma distributions. Using a Bayesian para...

In this paper, a novel interactive multiple model particle filter (IMMPF) is developed after a Bayesian estimator for maneuvering target tracking in clutter is derived theoretically. In this new algorithm, base state estimation and modal state estimation are completely separated to control the number of particles in each maneuvering mode. Only continuous-valued particles are used to numerically...

advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. in this article, we proposed a method to predict the rate of student enrollment in coming years. the data for this research were from data sets of voluntee...

The goal of this article is an exact Bayesian analysis of the Heston (1993) stochastic volatility model. We carefully study the affect different parameterizations of the latent volatility process and the parameters of the volatility process have on the convergence and the mixing behavior of the sampler. We apply the sampler to simulated data and to some DM/US$ exchange rate data.

The speed of convergence of the Expectation Maximization (EM) algorithm for Gaussian mixture model fitting is known to be dependent on the amount of overlap among the mixture components. In this paper, we study the impact of mixing coefficients on the convergence of EM. We show that when the mixture components exhibit some overlap, the convergence of EM becomes slower as the dynamic range among...

in this study digital image correlation (dic) technique combined with a high speed video system was used to predict movement of particles in a water model. comparing with particle-image velocimetry (piv) technique, it provides a low cost alternative approach to visualize flow fields and was successfully employed to predict the movement of particles in a water model at different submergence dept...