Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...

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 dif...

The Bayesian modeling formalism provides a principled means of incorporating prior information into a neural network model. One can extract greater power from the Bayesian formalism through use of priors which encode particular empirical or expert knowledge. These priors will generally be asymmetric with respect to the neural network output function. It would appear that in order to compute cor...