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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| Format: | Online |
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| Langue: | anglais |
| Publié: |
IntechOpen
2021
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| Sujets: | |
| Accès en ligne: | ONIX_20210420_9781838803865_2968 |
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| _version_ | 1869520648126070784 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-67608 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-676082024-04-04T14:41:18Z Bayesian Inference on Complicated Data Tang, Niansheng Mathematical modelling thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics 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. 2021-04-20T16:19:37Z 2021-04-20T16:19:37Z 2020 book ONIX_20210420_9781838803865_2968 9781838803865 9781838803858 9781839627040 https://directory.doabooks.org/handle/20.500.12854/67608 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/9218/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.83214 10.5772/intechopen.83214 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781838803865 9781838803858 9781839627040 IntechOpen 118 open access |
| spellingShingle | Mathematical modelling thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics Bayesian Inference on Complicated Data |
| title | Bayesian Inference on Complicated Data |
| title_full | Bayesian Inference on Complicated Data |
| title_fullStr | Bayesian Inference on Complicated Data |
| title_full_unstemmed | Bayesian Inference on Complicated Data |
| title_short | Bayesian Inference on Complicated Data |
| title_sort | bayesian inference on complicated data |
| topic | Mathematical modelling thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| topic_facet | Mathematical modelling thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| url | ONIX_20210420_9781838803865_2968 |