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Journal ArticleDOI

Being Sensitive to Uncertainty

TLDR
This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification inredictive modeling.
Abstract
Predictive modeling's effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification

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Citations
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Journal ArticleDOI

3D Slicer as an image computing platform for the Quantitative Imaging Network.

TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
Journal ArticleDOI

scikit-image: Image processing in Python

TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Journal ArticleDOI

Biopython: freely available Python tools for computational molecular biology and bioinformatics

TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Journal ArticleDOI

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

TL;DR: The pymatgen library as mentioned in this paper is an open-source Python library for materials analysis that provides a well-tested set of structure and thermodynamic analyses relevant to many applications, and an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments.
References
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Book

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

TL;DR: This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity.
Book

Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

TL;DR: In this paper, the authors present a method for sensitivity analysis of a fish population model using Monte Carlo filtering and variance-based methods, which is based on the Bayesian uncertainty estimation.
Book

Perturbation Analysis of Discrete Event Dynamic Systems

Yu-Chi Ho, +1 more
TL;DR: In this article, the authors present a short history of the Perturbation Analysis development and its application in discrete event dynamic systems (DEDS) and compare the performance of different types of DEDS.
Journal ArticleDOI

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method

TL;DR: Preliminaries Sensitivity Analysis and Stochastic Optimization of Discrete Event Static Systems (DESS) and Sensitivity analysis and stochastic optimization ofdiscrete Event Dynamic systems (DEDS).
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