Recently, El-Sherpieny et al (2020) suggested Type -II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regards being given to the choice priors and posterior risk associated with model. This paper fills this gap derived new LDmodel minimum posterior risk for selection priors. It derives a closed form expression Bayes estimates risks using Square error lo...

Abstract This paper addresses the problem of Bayesian estimation of the parameters of Erlang distribution under squared error loss function by assuming different independent informative priors as well as joint priors for both shape and scale parameters. The motivation is to explore the most appropriate prior for Erlang distribution among different priors. A comparison of the Bayes estimates and...

Consider a classical compound Poisson model. The safety loading can be positive, negative or zero. Explicit expressions for the distributions of the surplus prior and at ruin are given in terms of the ruin probability. Moreover, the asymptotic behaviour of these distributions as the initial capital tends to infinity are obtained. In particular, for positive safety loading the Cramer case, the c...

using a sample fiom burr type xu distribution, bayes prediction intervals are derived for the maximum and minimum of a future sample fromthe same distribution, but in the presence of a single outlier of the type 8,8. the prior of q is assumed to be the gamma conjugate. a real example is given to illustrate the procedure. also, the comparison between the values of the prediction bounds for diffe...

Purpose: MR image reconstruction exploits regularization to compensate for missing k-space data. In this work, we propose to learn the probability distribution of MR image patches with neural networks and use this distribution as prior information constraining images during reconstruction, effectively employing it as regularization. Methods: We use variational autoencoders (VAE) to learn the di...