Bayesian Networks Advances and Novel Applications
Bayesian networks (BN) have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis and assets and liabilities management, AI and robotics, transportation systems planning and optimization, political science analytics, law and forensic s...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2019
|
| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000naaaa2200000uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_67061 | ||
| 005 | 20210420 | ||
| 003 | oapen | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20210420s2019 xx |||||o ||| 0|eng d | ||
| 020 | |a intechopen.75254 | ||
| 020 | |a 9781839623233 | ||
| 020 | |a 9781839623226 | ||
| 020 | |a 9781839623240 | ||
| 040 | |a oapen |c oapen | ||
| 024 | 7 | |a 10.5772/intechopen.75254 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 072 | 7 | |a PBW |2 bicssc | |
| 100 | 1 | |a McNair, Douglas |4 edt | |
| 700 | 1 | |a McNair, Douglas |4 oth | |
| 245 | 1 | 0 | |a Bayesian Networks |b Advances and Novel Applications |
| 260 | |b IntechOpen |c 2019 | ||
| 300 | |a 1 electronic resource (136 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Bayesian networks (BN) have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis and assets and liabilities management, AI and robotics, transportation systems planning and optimization, political science analytics, law and forensic science assessment of agency and culpability, pharmacology and pharmacogenomics, systems biology and metabolomics, psychology, and policy-making and social programs evaluation. This strong and varied response results not least from the fact that plausibilistic Bayesian models of structures and processes can be robust and stable representations of causal relationships. Additionally, BNs' amenability to incremental or longitudinal improvement through incorporating new data affords extra advantages compared to traditional frequentist statistical methods. Contributors to this volume elucidate various new developments in these aspects of BNs. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/3.0/ |2 cc |4 https://creativecommons.org/licenses/by/3.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Applied mathematics |2 bicssc | |
| 653 | |a Mathematical modelling | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mts.intechopen.com/storage/books/7372/authors_book/authors_book.pdf |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/67061 |7 0 |z DOAB: description of the publication |