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Bayesian inference on complicated data / Niansheng Tang (2020)  

Public Titre : Bayesian inference on complicated data Type de document : Monographie Auteurs : Niansheng Tang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 118 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-83962-704-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] échantillonnage [Termes IGN] échantillonnage de Gibbs [Termes IGN] estimation bayesienne [Termes IGN] filtrage bayésien [Termes IGN] inférence statistique [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] théorème de Bayes Résumé : (éditeur) Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers. Note de contenu : 1- On the impact of the choice of the prior in Bayesian statistics 2- A brief tour of Bayesian sampling methods 3- A review on the exact Monte Carlo simulation 4- Bayesian analysis for random effects models 5- Bayesian inference of Gene regulatory network 6- Patient Bayesian inference: Cloud-based healthcare data analysis using constraint-based adaptive boost algorithm 7- The Bayesian posterior estimators under six loss functions for unrestricted and restricted parameter spaces Numéro de notice : 28590 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.83214 En ligne : https://doi.org/10.5772/intechopen.83214 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97937