For Modified-Weibull distribution we have obtained the Bayes Estimators of scale and shape parameters using Lindley's approximation (L-approximation) under various loss functions. The proposed estimators have been compared with the corresponding MLE for their risks based on corresponding simulated samples. Key-Words: Bayesian estimation, Lindley's approximation, Maximum likelihood estimates, Mo...

Data with a multimodal pattern can be analyzed using mixture model. In model, the most important step is determination of number components, because finding correct components will reduce error resulting Bayesian analysis, one method that used to determine reversible jump Markov chain Monte Carlo (RJMCMC). The RJMCMC for distributions have location and scale parameters or location-scale distrib...

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes. The generative network of WHAI has a hierarc...

In diseases caused by a deleterious gene mutation, knowledge of age-specific cumulative risks is necessary for medical management of mutation carriers. When pedigrees are ascertained through at least one affected individual, ascertainment bias can be corrected by using a parametric method such as the Proband's phenotype Exclusion Likelihood, or PEL, that uses a survival analysis approach based ...

The Weibull distribution has been previously applied to the mechanical and dieiectric failures of ceramics. In this paper, it is confirmed by experiment that this data treatment method is also valid for application in the dielectric failure of multilayer ceramic capacitors (MLCs) which have undergone screening. The Weibull modulus is found to be a useful parameter indicating the sharpness of th...

A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality directly related to the treatment and/or the treated condition, and a risk of late death influenced by several exogenous factors. The parametric mixture is based on Weibull distributions for both components. Different set...

The Weibull distribution has wide applications, particularly in life distribution. There exists a large body of literature on statistical methods based on the Weibull distribution, but most of the methods require numerical integration or Monte Carlo methods to develop the confidence intervals for model parameters. The reason for the large volume of methods revolves around the fact that there is...

We derive a closed form solution for the Kullback-Leibler divergence between two Weibull distributions. These notes are meant as reference material and intended to provide a guided tour towards a result that is often mentioned but seldom made explicit in the literature. 1 The Weibull Distribution TheWeibull distribution is the type III extreme value distribution; its probability density functio...

Starting from on a recently introduced Gaussian class bivariate Weibull stochastic model, the probability density and the cumulative distribution functions of the product (Z1 Z2) c and the ratio (Z1/Z2) c, when Z1 and Z2 are correlated Weibull random variables belonging to this class (c > 0), are derived in closed form. Moreover, using the inequality between arithmetic and geometric mean, a uni...