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Data science: Measuring uncertainties / Carlos Alberto De Bragança Pereira (2021)  

Public Titre : Data science: Measuring uncertainties Type de document : Monographie Auteurs : Carlos Alberto De Bragança Pereira, Éditeur scientifique ; Adriano Polpo, Éditeur scientifique ; Agatha Rodrigues, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 256 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0793-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] algorithme espérance-maximisation [Termes IGN] analyse de groupement [Termes IGN] données massives [Termes IGN] entropie maximale [Termes IGN] équation de Riccati [Termes IGN] estimation bayesienne [Termes IGN] filtre de Kalman [Termes IGN] inférence statistique [Termes IGN] information sémantique [Termes IGN] intelligence artificielle [Termes IGN] logique floue [Termes IGN] science des données Résumé : (éditeur) With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems. Note de contenu : 1- An integrated approach for making inference on the number of clusters in a mixture model 2- Universal sample size invariant measures for uncertainty quantification in density estimation 3- Prior sensitivity analysis in a semi-parametric integer-valued time series model 4- The decomposition and forecasting of mutual investment funds using singular spectrum analysis 5- Channels’ confirmation and predictions’ confirmation: From the medical test to the raven paradox 6- On a class of tensor Markov fields 7- Objective Bayesian inference in probit models with intrinsic priors using variational approximations 8- A new multi-attribute emergency decision-making algorithm based on intuitionistic fuzzy cross-entropy and comprehensive grey correlation analysis 9- Cointegration and unit root tests: A fully Bayesian approach 10- A novel perspective of the Kalman filter from the Renyi entropy 11- Application of cloud model in qualitative forecasting for stock market trends 12- A novel comprehensive evaluation method for estimating the bank profile shape and dimensions of stable channels using the maximum entropy principle Numéro de notice : 28636 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0793-4 En ligne : https://doi.org/10.3390/books978-3-0365-0793-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99694