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dc.contributor.editorTang, Niansheng
dc.date.accessioned2021-04-20T16:19:37Z
dc.date.available2021-04-20T16:19:37Z
dc.date.issued2020
dc.identifierONIX_20210420_9781838803865_2968
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/67608
dc.description.abstractDue 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematicsen_US
dc.subject.otherMathematical modelling
dc.titleBayesian Inference on Complicated Data
dc.typebook
oapen.identifier.doi10.5772/intechopen.83214
oapen.relation.isPublishedBy78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6
oapen.relation.isbn9781838803865
oapen.relation.isbn9781838803858
oapen.relation.isbn9781839627040
oapen.imprintIntechOpen
oapen.pages118


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