Geostatistical Analysis in Bayes Spaces: Probability Densities and Compositional Data

Geostatistical Analysis of Spatial Data

First, geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes. Various interpolation (kriging) techniques capitalize on the spatial correlation between observations to predict attribute values at unsampled locations using information related to one or several attributes. An important contribution of geost...

Geostatistical Analysis of Monitoring Data

Geostatistical Computing in PSInSAR Data Analysis

The presented paper describes the geostatistical analysis of PSInSAR data. This analysis was preceded by short description of PSInSAR technique. The geostatistical computations showed in this article were performed with the R open-source software containing the gstat package. The analysis contains variograms computing (directional variograms) and ordinary kriging interpolation. The computationa...

Probability , Conditional Probability and Bayes Formula

The intuition of chance and probability develops at very early ages.1 However, a formal, precise definition of the probability is elusive. If the experiment can be repeated potentially infinitely many times, then the probability of an event can be defined through relative frequencies. For instance, if we rolled a die repeatedly, we could construct a frequency distribution table showing how many...

Asymptotically minimax Bayes predictive densities

fθ log (fθ/f̂) is used to examine various ways of choosing prior distributions; the principal type of choice studied is minimax. We seek asymptotically least favorable predictive densities for which the corresponding asymptotic risk is minimax. A result resembling Stein’s paradox for estimating normal means by the maximum likelihood holds for the uniform prior in the multivariate location family...