A characterization of the normal distribution using stationary max-stable processes
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The large variability of the drop size distribution (DSD) in space and time must be taken into account to improve remote sensing of precipitation. The ability to simulate a large number of 2D fields of DSD sharing the same statistical properties provides a ...
To aid assessments of climate change impacts on water related activities in the case study regions (CSRs) of the EC funded project SWURVE, estimates of uncertainty in climate model data need to be developed. In this paper, two methods to estimate uncertain ...
This article introduces a new measure of travel time reliability for implementation in the dynamic routing algorithm of an intelligent car navigation system. The measure is based on the log-normal distribution of travel time on a link and consists of two i ...
We describe a set of probability distributions, dubbed compressible priors, whose independent and identically distributed (iid) realizations result in p-compressible signals. A signal x in R^N is called p-compressible with magnitude R if its sorted coeffic ...
In this paper we address an observational validation of recent theoretical results on the structure of the probability density function (pdf) of daily streamflows through the analysis of data pertaining to several catchments covering various sizes, climati ...
This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Poisson Stochastic Processes. The experiments are performed over 27 hours of material: prel ...
Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...
A model for the yield stress of particulate suspensions is presented that incorporates microstructural parameters taking into account volume fraction of solids, particle size, particle size distribution, maximum packing, percolation threshold, and interpar ...
Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two cond ...
This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Poisson Stochastic Processes. Preliminary experiments address the problem of segmenting aut ...