Available values of the surface tension of 81 common fluids were fitted by using the same model presently used in the REFPROP program V9.0 by NIST. A set of data was built for every fluid by including mainly those values given in the DIPPR and DETHERM databases. For some fluids, other available sources of data were added in order to obtain adequate sets. For every fluid, we checked the accuracy of the REFPROP program and we made a new fit in order to improve the performance of the correlation. We found good general agreement between the REFPROP predictions and the data for 44 fluids, and therefore only very slight improvements are made for them. For the other 37 fluids, the REFPROP correlation can be more clearly improved. In particular, our new correlation is significantly more accurate for ammonia, deuterium, ethanol, and neon, because the version of REFPROP used gives values in clear disagreement with other data sources.

In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distributions and also allow us to propose new estimators for the parameters of the tail distribution. Specifically, three cases of stable distributions were considered: Normal, Lévy and Cauchy distributions, as main examples of the different behaviors of the tails of a distribution. Nevertheless, the methods would be applicable to many other baseline distributions through finding relations between baseline and GPD parameters via studies of simulations. To illustrate this situation, we study the application of the methods with real data of air pollution in Badajoz (Spain), whose baseline distribution fits a Gamma, and show that the baseline methods improve estimates compared to the Bayesian Metropolis–Hastings algorithm.