Bayesian Inference on Complicated Data / / Niansheng Tang.
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...
Saved in:
VerfasserIn: | |
---|---|
Place / Publishing House: | London : : IntechOpen,, 2020. |
Year of Publication: | 2020 |
Language: | English |
Physical Description: | 1 online resource (118 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993603432404498 |
---|---|
ctrlnum |
(CKB)5400000000044559 (NjHacI)995400000000044559 (EXLCZ)995400000000044559 |
collection |
bib_alma |
record_format |
marc |
spelling |
Tang, Niansheng, author. Bayesian Inference on Complicated Data / Niansheng Tang. London : IntechOpen, 2020. 1 online resource (118 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Description based on publisher supplied metadata and other sources. 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. English. Applied mathematics. 1-83962-704-2 |
language |
English |
format |
eBook |
author |
Tang, Niansheng, |
spellingShingle |
Tang, Niansheng, Bayesian Inference on Complicated Data / |
author_facet |
Tang, Niansheng, |
author_variant |
n t nt |
author_role |
VerfasserIn |
author_sort |
Tang, Niansheng, |
title |
Bayesian Inference on Complicated Data / |
title_full |
Bayesian Inference on Complicated Data / Niansheng Tang. |
title_fullStr |
Bayesian Inference on Complicated Data / Niansheng Tang. |
title_full_unstemmed |
Bayesian Inference on Complicated Data / Niansheng Tang. |
title_auth |
Bayesian Inference on Complicated Data / |
title_new |
Bayesian Inference on Complicated Data / |
title_sort |
bayesian inference on complicated data / |
publisher |
IntechOpen, |
publishDate |
2020 |
physical |
1 online resource (118 pages) |
isbn |
1-83962-704-2 |
callnumber-first |
Q - Science |
callnumber-subject |
QA - Mathematics |
callnumber-label |
QA300 |
callnumber-sort |
QA 3300 T364 42020 |
illustrated |
Not Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
510 - Mathematics |
dewey-ones |
519 - Probabilities & applied mathematics |
dewey-full |
519 |
dewey-sort |
3519 |
dewey-raw |
519 |
dewey-search |
519 |
work_keys_str_mv |
AT tangniansheng bayesianinferenceoncomplicateddata |
status_str |
n |
ids_txt_mv |
(CKB)5400000000044559 (NjHacI)995400000000044559 (EXLCZ)995400000000044559 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Bayesian Inference on Complicated Data / |
_version_ |
1796653209502089217 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01610nam a2200289 i 4500</leader><controlfield tag="001">993603432404498</controlfield><controlfield tag="005">20230626160721.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr |||||||||||</controlfield><controlfield tag="008">230626s2020 enk o 000 0 eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000044559</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(NjHacI)995400000000044559</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000044559</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">NjHacI</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="c">NjHacl</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA300</subfield><subfield code="b">.T364 2020</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">519</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tang, Niansheng,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Inference on Complicated Data /</subfield><subfield code="c">Niansheng Tang.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">London :</subfield><subfield code="b">IntechOpen,</subfield><subfield code="c">2020.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (118 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Applied mathematics.</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">1-83962-704-2</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-07-06 03:33:58 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338089250004498&Force_direct=true</subfield><subfield code="Z">5338089250004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338089250004498</subfield></datafield></record></collection> |