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: | |
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| Format: | Électronique Chapitre de livre |
| Langue: | anglais |
| Publié: |
IntechOpen
2020
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| Sujets: | |
| Accès en ligne: | DOAB: download the publication DOAB: description of the publication |
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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. |
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| Description matérielle: | 1 electronic resource (118 p.) |
| ISBN: | intechopen.83214 9781838803865 9781838803858 9781839627040 |
| Accès: | Open Access |