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Bayesian Computational Methods of the Logistic Regression Model
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Abstract<p>In this paper, we will discuss the performance of Bayesian computational approaches for estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo (MCMC) algorithms was the base estimation procedure. We present two algorithms: Random Walk Metropolis (RWM) and Hamiltonian Monte Carlo (HMC). We also applied these approaches to a real data set.</p>
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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Engineering
Computational Analysis of Turbulent Natural Convection in Water Filled Tall Enclosure
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In this study, the turbulent buoyancy driven fluid flow and heat transfer in a differentially heated rectangular enclosure filled with water is quantified numerically. The two dimensional governing differential equations are discretized using the finite volume method. SIMPLE algorithm is employed to obtain stabilized solution for high Rayleigh numbers by a computational code written in FORTRAN language. A parametric study is undertaken and the effect of Rayleigh numbers (1010 to 1014), the aspect ratio (30, 40 and 50), and the tilt angle (10o to 170o ) on fluid flow and heat transfer are investigated. The results of the adopted model in the present work is compared with previously published results and a qualitative agreement and a good

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Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Design Sampling Plan when Life Time Follows Logistic Distribution
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Design sampling plan was and still one of most importance subjects because it give lowest cost  comparing with others, time live statistical distribution should be known to give best estimators for  parameters of sampling plan and get best sampling plan.

Research dell with design sampling plan when live time distribution follow Logistic distribution with () as location and shape parameters, using these information can help us getting (number of groups, sample size) associated with reject or accept the Lot

Experimental results for simulated data shows the least number of groups and sample size needs to reject or accept the Lot with certain probability of

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Publication Date
Tue Apr 20 2021
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Bayesian Structural Time Series for Forecasting Oil Prices
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There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we st

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Publication Date
Sun Apr 06 2008
Journal Name
Diyala Journal For Pure Science
Preliminary Test Bayesian –Shrunken Estimators for the Mean of Normal Distribution with Known Variance
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Publication Date
Tue Mar 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
Estimate the Nonparametric Regression Function Using Canonical Kernel
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    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

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Publication Date
Thu Aug 01 2013
Journal Name
Micron
Computational investigation of electron path inside SEM chamber in mirror effect phenomenon
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Publication Date
Sun Feb 20 2022
Journal Name
Papers In Physics
Electronic and optical properties of nickel-doped ceria: A computational modelling study
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Cerium oxide (CeO2), or ceria, has gained increasing interest owing to its excellent catalytic applications. Under the framework of density functional theory (DFT), this contribution demonstrates the eect that introducing the element nickel (Ni) into the ceria lattice has on its electronic, structural, and optical characteristics. Electronic density of states (DOSs) analysis shows that Ni integration leads to a shrinkage of Ce 4f states and improvement of Ni 3d states in the bottom of the conduction band. Furthermore, the calculated optical absorption spectra of an Ni-doped CeO2 system shifts towards longer visible light and infrared regions. Results indicate that Ni-doping a CeO2 system would result in a decrease of the band gap. Finally,

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Publication Date
Sun Feb 20 2022
Journal Name
Papers In Physics
Electronic and optical properties of nickel-doped ceria: A computational modelling study
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Cerium oxide CeO2, or ceria, has gained increasing interest owing to its excellent catalytic applications. Under the framework of density functional theory (DFT), this contribution demonstrates the effect that introducing the element nickel (Ni) into the ceria lattice has on its electronic, structural, and optical characteristics. Electronic density of states (DOSs) analysis shows that Ni integration leads to a shrinkage of Ce 4f states and improvement of Ni 3d states in the bottom of the conduction band. Furthermore, the calculated optical absorption spectra of an Ni-doped CeO2 system shifts towards longer visible light and infrared regions. Results indicate that Ni-doping a CeO2 system would result in a decrease of the band gap. Finally,

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Publication Date
Thu Mar 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Using Truncated Test for Finding the Parameters of Single Sampling Plan under Distribution of Log-Logistic
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A group of acceptance sampling to testing the products was designed when the life time of an item follows a log-logistics distribution. The minimum number of groups (k) required for a given group size and acceptance number is determined when various values of Consumer’s Risk and test termination time are specified. All the results about these sampling plan and probability of acceptance were explained with tables.

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Publication Date
Mon Apr 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Bayesian Estimators of the parameter and Reliability Function of Inverse Rayleigh Distribution" A comparison study "
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     In this paper, Bayesian estimator for the parameter and reliability function of inverse Rayleigh distribution (IRD) were obtained Under three types of loss function, namely, square error loss function (SELF), Modified Square error loss function (MSELF) and Precautionary loss function (PLF),taking into consideration the  informative and non- informative  prior. The performance of such estimators was assessed on the basis of mean square error (MSE) criterion by performing a Monte Carlo simulation technique.

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