Variability dependent modeling provides a way of handling the impact of some variability sources in the modeling. In many cases, the variability factor is estimated in a deterministic way, leading to a mere selection of the most adequate model. However, there are always some uncertainty in the estimation of the variability sources which may induce a sub optimal model selection. This paper consi...

Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the 'uncertainty' of a solution, that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is i...

The use of detailed models for bioprocess design and control has been limited as accurate estimation of model parameters is often difficult. In this paper, the parameter estimation problem for a mechanistic model of the production of a biopolymer, poly-β-hydroxybutyrate (PHB), is examined in detail. Parameter estimation was undertaken using previously published data. Three parameter sets were o...

Uncertainty decoding and uncertainty propagation, or error propagation, techniques have emerged as a powerful tool to increase the accuracy of automatic speech recognition systems by employing an uncertain, or probabilistic, description of the speech features rather than the usual point estimate. In this paper we analyze the uncertainty generated in the complex Fourier domain when performing sp...

Diffusion-based passive samplers are increasingly used for water quality monitoring. While the overall method robustness and reproducibility for passive samplers in water are widely reported, there has been a lack of a detailed description of uncertainty sources. In this paper an uncertainty budget for the determination of fully labile Cu in water using a DGT passive sampler is presented. Uncer...

Computer-Intensive methods for estimation assessment provide valuable information concerning the adequacy of applied probabilistic models. The bootstrap method is an extensive computational approach to uncertainty estimation based on resampling and statistical estimation. It is a powerful tool, especially when only a small data set is used to predict the behaviour of systems or processes. This ...

The seminal work on set membership state estimation [1] F. C. Schweppe. Recursive state estimation: unknown but bounded errors and system inputs. [2] D. P. Bertsekas and I. B. Rhodes. Recursive state estimation for a set-membership description of uncertainty.

A software tool, AuvTool, for statistical analysis of variability and uncertainty associated with fitting distributions to data sets is being developed for use with the Stochastic Human Exposure Dose Simulation (SHEDS) modeling framework. It is also generally applicable for quantifying variability and uncertainty in data sets used for risk assessment, emissions estimation and other quantitative...

[1] The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-...