Inferential methods of statistical distributions have reached a high level of interest in recent years. However, in real life, data can follow more than one distribution, and then mixture models must be fitted to such data. One of which is a finite mixture of Rayleigh distribution that is widely used in modelling lifetime data in many fields, such as medicine, agriculture and engineering. In this paper, we proposed a new Bayesian frameworks by assuming conjugate priors for the square of the component parameters. We used this prior distribution in the classical Bayesian, Metropolis-hasting (MH) and Gibbs sampler methods. The performance of these techniques were assessed by conducting data which was generated from two and three-component mixt

In the present work, a program for calculating the coefficients of the Aplanatic Cassegrain Telescope (ACT) system, free from the effects of spherical and coma aberrations, were constructed. In addition, the two-mirrors of the optical system, as aspherical surfaces, were adopted. This means, that the two-equations of the mirrors are assumed to be polynomial function of five even terms only. The numerical method, least-squares curve fitting method to calculate the two-mirror coefficients system, was adopted. For choosing the values and ratios that give the best results, Rayleigh Criterion (Rayleigh Limit), for purpose of comparison and preference, was adopted.

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

Objective: In order to evaluate the effect of different typed of Separating Medium on the roughness of the fitted
tissue surface of acrylic denture base.
Methodology: Chosen three types of separating medium (Group A Tin foil), (Group B Detery Isolant),(Group C
Cold Mould Seal),used 30 samples of hot cure acrylic resin ,10 samples for each group, after complete curing of
these samples , Profilometer device was used to measure the surface roughness of each sample in all groups.
Results: Using One Way ANOVA Test and LSD test, the results were highly significant in differences among all
groups. Although (Group A) showing lest roughness, (Group B) showing a satisfactory result of roughness, While
(Group C) Showing the hig

In this work; copper oxide films (CuO) were fabricated by PLD. The films were analyzed by UV-VIS absorption spectra and their thickness by using profilometer. Pulsed Nd:YAG laser was used for prepared CuO thin films under O₂ gas environment with varying both pulse energy and annealing temperature. The optical properties of   as-grown film such as optical transmittance spectrum, refractive index and energy gap has been measured experimentally and the effects of laser pulse energy  and annealing temperature on it were studied. An inverse relationship between energy gap and both annealing temperature and pulse energy was observed.

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the