Bayesian Inference on Complicated Data

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 method...

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Autres auteurs: Tang, Niansheng (Éditeur intellectuel)
Format: Électronique Chapitre de livre
Langue:anglais
Publié: IntechOpen 2020
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Résumé: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.
Description matérielle:1 electronic resource (118 p.)
ISBN:intechopen.83214
9781838803865
9781838803858
9781839627040
Accès:Open Access