The aim of this work was to show how to construct maps of anthropogenic contamination of
agricultural soils in Czech Republic by risk elements. The geochemical datasets for this work …

Bayes spaces were initially designed to provide a geometric framework for the modeling and
analysis of distributional data. It has recently come to light that this methodology can be …

In the context of functional data analysis, probability density functions as non-negative
functions are characterized by specific properties of scale invariance and relative scale …

While statistical modeling of distributional data has gained increased attention, the case of
multivariate distributions has been somewhat neglected despite its relevance in various …

Distributional data have recently become increasingly important for understanding
processes in the geosciences, thanks to the establishment of cost-efficient analytical …

We develop a new method for multivariate scalar on multidimensional distribution
regression. Traditional approaches typically analyze isolated univariate scalar outcomes or …

Distributional data analysis, concerned with the statistical analysis of data objects consisting
of random probability distributions in the framework of functional data analysis (FDA), has …

The process of supervised classification when the data set consists of probability density
functions is studied. Due to the relative information contained in densities, it is necessary to …

Performing inference in Bayesian models requires sampling algorithms to draw samples
from the posterior. This becomes prohibitively expensive as the size of data sets increase …

Bayes spaces were initially designed to provide a geometric framework for the modeling and
analysis of distributional data. It has recently come to light that this methodology can be …