Document Details

Document Type : Thesis 
Document Title :
Additions whale predict Albaezi and their applications in parametric estimation of some functions probability density function
إضافات حوت دالة التنبؤ الباييزي و تطبيقاتها في التقدير المعلمي لبعض دوال الكثافة الاحتمالية
 
Subject : Additions whale predict Albaezi and their applications in parametric estimation of some functions probability density function 
Document Language : Arabic 
Abstract : in view of the fact Bayesian predictive density function study is a new subject in most fields of scientific research ,the aim of this thesis , which consists of seven chapters, is to study this function and some finding . In some cases it is difficult to computer or from the prediction periods so we studied proposals for approximation of predictive function It is well known that prediction of the outcome of the future ) should xt which is described by the function P(y|experimencome close to portability density function P(y|ϴ) . when the sample increases using Kullback and leibler measure of divergence. Chapter 1 consists of abckground study for Bayesian methods. Our study depends on tow probability distributions are important in practical applications and their newness in this field In chapter 2 we did a brief survey about distribution characteristics, statistical methods and applications’. In charter 3 and 4 we use Bayesian method of estimation to estimate a parameter of each of the underlying distribution when the other parameter is known , which takes probability density function from the following distribution : uniform, exponential and inverted gamma and including also non-informative prior. Chapter 3 included the estimation of tow parameters of inverse Gaussian distribution in case of non-informative prior density and also in case when one parameter has uniform distribution and the other has a gamma distribution. In chapter 4 we get the estimation of tow parameters of Weibull distribution in case that prior density function for one parameter has a discrete mass function (for the shape parameter ) and inverted gamma ( for the quais -scale parameter). the posterior densities functions found in chapter 3 and 4 are used in chapter 5 to find Bayesian predictive densities for both distributions studied and anew proper from is found . In chapter 6 we obtained proposal for approximation of predictive function whish we mentioned in chapter 1. For the tow distributions , we are studying we also give tow practical applications: One by generating data from inverse Gaussian distribution and the other by using data from Weibull distribution sources . Graphical comparison using computer is shown with the original density function , approximated predictive density and Bayesian predictive function with prior densities : Exponential , non-informative in the case of the inverse Gaussian distribution and inverted gamma function , non- informative in the case of Weibull distribution. It is worth mentioning here that our new findings were good . in chapter 7 we obtained the approximated Bayesian estimate and the posterior variance for the shape parameter of the inverse Gaussian distribution with prior densities: uniform , exponential and non -informative similarly we obtained the approximated Bayesian estimate and the variance for the ( quasi- scale parameter) for Weibull distribution . All results of this chapter are presented in tables . The result show that the approximate estimation method for the parameter can be applied easily and give accurate result 
Supervisor : DR.AYSHA SAYED RAJAB 
Thesis Type : Doctorate Thesis 
Publishing Year : 1416 AH
1996 AD 
Added Date : Sunday, February 1, 2015 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
جواهر عبدالرحمن باصبرينBASABRAIN, JAWAHER ABDULRAHMANInvestigatorDoctorate 

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