The last decade has seen the success of stochastic parameterizations in short-term, mediumrange and seasonal ensembles: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations no...

We consider flows in fractured media, described by Discrete Fracture Network (DFN) models. We perform an Uncertainty Quantification analysis, assuming the fractures’ transmissivity coefficients to be random variables. Two probability distributions (log-uniform and log-normal) are used within different laws that express the coefficients in terms of a family of independent stochastic variables; t...

pregabalin (prg) is a new antiepileptic drug and aceclofenac (ace) is a potent non-steroidal anti-inflammatory drug. these drugs in combination are used for treatment of partial seizures and neuropathic pains. a simple and precise assay method by rp-hplc was developed and validated for estimation of prg and ace in acenac-n tablet. analyses of commercial tablet, acenac-n were performed using jas...

In this article, we recall different approaches to the constraint-based, symbolic analysis of hybrid discrete-continuous systems and combine them to a technology able to address hybrid systems exhibiting both non-deterministic and probabilistic behavior akin to infinite-state Markov decision processes. To enable mechanized analysis of such systems, we extend the reasoning power of arithmetic sa...

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work we introduce a novel formulation of DGPs b...

Abstract The nonparametric probabilistic method (NPM) for modeling and quantifying model-form uncertainties is a physics-based, computationally tractable, machine learning performing uncertainty quantification model updating. It extracts from data information not captured by deterministic, high-dimensional (HDM) of dimension N infuses it into counterpart stochastic, hyperreduced, projection-bas...

different software tools for quantification of myocardial perfusion spect (mps) studies are routinely used. several perfusion parameters can be computed automatically.  interpretation of the mps should start with visual inspection of the rotating planar images, visual analysis of reconstructed spect slices and then quantitative analysis to confirm the visual impression. quantification should be...

We will discuss several techniques for obtaining point estimates and confidence intervals for steady-state performance measures. Let (X(t): t≥ 0) be a GSMP with (discrete) state space S that represents the underlying stochastic process of a simulation. Fix a real-valued function f defined on S and set Y(t) = f(X(t)) for t ≥ 0. There are several possible ways to define steady-state performance m...

An unknown transient heat source in a three-dimensional participating medium is reconstructed from temperature measurements using a Bayesian inference method. The heat source is modeled as a stochastic process. The joint posterior probability density function (PPDF) of heat source values at consecutive time points is computed using the Bayes’ formula. The errors in thermocouple readings are mod...