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|Title||Bayesian Inference on Finite Mixtures of Exponential Distributions|
|Title in Arabic||الاستدلال الباييزي حول مزيج من التوزيعات الاسية المحدودة|
Mixed distributions are widely used to model data in which each observation is assumed to come from one of a number of different groups. In this thesis, we investigate the Bayesian estimation for the finite exponential mixture model using the Gibbs sampler as an important one of the MCMC methods. Our approach in this thesis depends on using the Gibbs sampler to simulate a Markov chain which has the posterior density as its long-run (stationary) distribution. Then we use the resulting sample to make the suitable Bayesian computations and draw conclusion about the unknown parameters of the exponential mixture model. We conclude this thesis by presenting a real data example to illustrates our methodology.
|Publisher||الجامعة الإسلامية - غزة|
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