In this paper the Weibull fading channel model is described and some integral characteristics of Weibull distribution are analyzed. For analytical and numerical evaluation of system performance, the Weibull probability density functions (pdf) are analyzed like particular solutions of corresponding differential equation, while the existence of singular solution is considered and analyzed under d...

In this paper we will develop further the sequential life test approach presented in a previous article by [1] using an underlying two parameter Inverse Weibull sampling distribution. The location parameter or minimum life will be considered equal to zero. Once again we will provide rules for making one of the three possible decisions as each observation becomes available; that is: accept the n...

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

The Weibull distribution is widely used in reliability and life data analysis due to its versatility. And, this distribution has been considered as a popular life time distribution which describes modeling phenomena with monotonic failure rates of components. Depending on the values of the parameters it can be used to model a variety of life behaviors. In this paper, reliability measures such a...

In this study, we introduce a new familyof models for lifetime data called generalized extended Weibullpower series family of distributions by compoundinggeneralizedextended Weibull distributions and power series distributions. The compounding procedure follows the same setup carried out by Adamidis (1998). The proposed family contains all types of combinations between truncated discrete with g...

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...

Different functions, each in the form of a modified Weibull cumulative distribution function (cdf), were joined together to form a segmented cdfto approximate diameter distributions in forest stands. Estimates of five percentile points (the 0th, 25th, 50th, 75th, and 100th percentiles) were used to determine a segmented cdf. The segmented cdf and the Weibull distribution were fit to diameter da...

Satellite Communication Networks in modern days has higher frequencies above 10GHZ.Frequencies more than 10GHZ causes rain attenuation. In this paper, a channel model is defined to synthesize rain attenuation of 10GHZ and above. Previously, for satellite communication networks, Maseng-Bakken(M-B)model is used which is a time series synthesizer proposed based on Stochastic Differential equations...

Numerical simulations of time-dependent stochastic failure of fiber network have been performed by using a central-force, triangular lattice model. This two-dimensional (2D) network can be seen as the next level of structural hierarchy to fiber bundles, which have been investigated for many years both theoretically and numerically. Unlike fiber bundle models, the load sharing of the fiber netwo...

We propose a simple close-to-normal approximation to a Weibull random variable r.v. and consider the problem of estimation of upper prediction limit UPL that includes at least l out of m future observations from a Weibull distribution at each of r locations, based on the proposed approximation and the well-known Box-Cox normal approximation. A comparative study based on Monte Carlo simulations ...