Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology science. In this work, we investigate the fusion multiple probability density functions (pdfs) continuous random variable or vector. Although case variables problem pdf frequently arise multisensor processing, statistical inference, machine learning, universally accep...

The inverse Gaussian distribution (IGD) is a well known and often used probability distribution for which fully reliable numerical algorithms have not been available. We develop fast, reliable basic probability functions (dinvgauss, pinvgauss, qinvgauss and rinvgauss) for the IGD that work for all possible parameter values and which achieve close to full machine accuracy. The most challenging t...

Meteorological stations usually contain some missing data for different reasons.There are several traditional methods for completing data, among them bivariate and multivariate linear and non-linear correlation analysis, double mass curve, ratio and difference methods, moving average and probability density functions are commonly used. In this paper a blended model comprising the bivariate expo...

The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and have included the Gaussian distribution in Classical Evolutionary Programming, the Cauchy distribution in Fast Evolutionary Programming, and the Lévy distribution. In this paper, we automatically design the mutation operators (probability distributions) using Geneti...

Conditionalization, i.e., computation of a conditional probability distribution given a joint probability distribution of two or more random variables is an important operation in some probabilistic database models. While the computation of the conditional probability distribution is straightforward when the exact point probabilities are involved, it is often the case that such exact point prob...