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Uncertain : the wisdom and wonder of being unsure
\"Featuring cutting-edge research and in-depth reporting, this paradigm-shifting book shows us how to skillfully confront the unexpected and unknown, and how to seek not-knowing in the service of curiosity, wisdom, and discovery\"-- Provided by publisher.
Book
Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis
Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.
Journal Article
Editor's Note
This issue of Law & Society Review goes to press at a time of uncertainty, loss, and dislocation unprecedented in many of the countries where our readers, authors, reviewers, and editorial team reside.
Journal Article
How to measure uncertainty in uncertainty sampling for active learning
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.
Journal Article
Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review
Traditional structural uncertainty analysis is mainly based on probability models and requires the establishment of accurate parametric probability distribution functions using large numbers of experimental samples. In many actual engineering problems, the probability distributions of some parameters can be established due to sufficient samples available, whereas for some parameters, due to the lack or poor quality of samples, only their variation intervals can be obtained, or their probability distribution types can be determined based on the existing data while some of the distribution parameters such as mean and standard deviation can only be given interval estimations. This thus will constitute an important type of probability-interval hybrid uncertain problem, in which the aleatory and epistemic uncertainties both exist. The probability-interval hybrid uncertainty analysis provides an important mean for reliability analysis and design of many complex structures, and has become one of the research focuses in the field of structural uncertainty analysis over the past decades. This paper reviews the four main research directions in this area, i.e., uncertainty modeling, uncertainty propagation analysis, structural reliability analysis, and reliability-based design optimization. It summarizes the main scientific problems, technical difficulties, and current research status of each direction. Based on the review, this paper also provides an outlook for future research in probability-interval hybrid uncertainty analysis.
Journal Article
Data uncertainty and important measures
The first part of the book defines the concept of uncertainties and the mathematical frameworks that will be used for uncertainty modeling. The application to system reliability assessment illustrates the concept. In the second part, evidential networks as a new tool to model uncertainty in reliability and risk analysis is proposed and described. Then it is applied on SIS performance assessment and in risk analysis of a heat sink. In the third part, Bayesian and evidential networks are used to deal with important measures evaluation in the context of uncertainties.-- Provided by Publisher.
Book
Kaj trenutno vemo o prihodnjem razvoju livarske industrije v Evropi?
Zaradi trenutnih razmer v svetu se moramo vprašati, kako zanesljive so dejansko izjave o prihodnjem gospodarskem in družbenem razvoju. To vprašanje si je treba vedno znova zastavljati, pri tem pa upoštevati, za razliko od znanstvenih problemov, kjer so dejstva neodvisna od z njimi povezanih izjav, da gospodarski razvoj temelji na odločitvah ljudi, podjetij, političnih in številnih drugih institucij, ki oblikujejo mnenja in s svojimi idejami spreminjajo družbene sisteme. Pri tem gre lahko za nezavedne spremembe, ki pa jih je treba obravnavati zavestno in načrtno, npr. z ustrezno intenzivno oglaševalsko kampanjo. Ta povezava med razmišljanjem in dejanskim stanjem v gospodarstvu vodi do dejstva, da so »trgi po svoji naravi nestabilni« [1], zato je treba izjave jemati z določeno stopnjo negotovosti. Posebej aktualen primer razvoja trgov, ki se odvija drugače, kot so si zamislili odgovorni ljudje v politiki in gospodarstvu, je razvoj elektromobilnosti v Nemčiji, ki ima katastrofalne posledice za avtomobilsko industrijo in pomembne dobaviteljske panoge. Čeprav se zavedamo, da so trgi nestabilni in da se lahko včasih razvijajo zelo hitro in na skoraj nepredvidljiv način, je zanimivo razmisliti, ali je mogoče opredeliti dejstva, ki kljub negotovosti dajejo določen vpogled v možni prihodnji razvoj.
Journal Article