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This paper presents
a hierarchical Bayesian approach to the estimation of components’ reliability
(survival) using a Weibull model for each of them. The proposed method can be
used to estimation with general survival censored data, because the estimation
of a component’s reliability in a series (parallel) system is equivalent to the
estimation of its survival function with right- (left-) censored data. Besides
the Weibull parametric model for reliability data, independent gamma
distributions are considered at the first hierarchical level for the Weibull
parameters and independent uniform distributions over the real line as priors
for the parameters of the gammas. In order to evaluate the model, an example
and a simulation study are discussed.
In the era of big data, huge volumes of data
are generated from online social networks, sensor networks, mobile devices, and
organizations’ enterprise systems. This phenomenon provides organizations with unprecedented
opportunities to tap into big data to mine valuable business intelligence. However,
traditional business analytics methods may not be able to cope with the flood of
big data. The main contribution of this paper is the illustration of the development
of a novel big data stream analytics framework named BDSASA that leverages a probabilistic
language model to analyze the consumer sentiments embedded in hundreds of millions
of online consumer reviews. In particular, an inference model is embedded into the
classical language modeling framework to enhance the prediction of consumer sentiments.
The practical implication of our research work is that organizations can apply our
big data stream analytics framework to analyze consumers’ product preferences, and
hence develop more effective marketing and production strategies.
This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.