東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Bayesian compendium

Oijen, Marcel van.

FindBook

Google Book

Amazon

博客來

Bayesian compendium

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian compendium/ by Marcel van Oijen.
作者:	Oijen, Marcel van.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xvi, 265 p. :ill. (some col.), digital ;24 cm.
內容註:	- 1. Science and Uncertainty -- 2. Bayesian Inference -- 3. Assigning a Prior Distribution -- 4. Assigning a Likelihood Function -- 5. Deriving the Posterior Distribution -- 6. Markov Chain Monte Carlo Sampling (MCMC) -- 7. Sampling from the Posterior Distribution by MCMC -- 8. MCMC and Multivariate Models -- 9. Bayesian Calibration and MCMC: Frequently Asked Questions -- 10. After the Calibration: Interpretation, Reporting, Visualisation -- 11. Model Ensembles: BMC and BMA -- 12. Discrepancy -- 13. Approximations to Bayes -- 14.Thirteen Ways to Fit a Straight Line -- 15. Gaussian Processes and Model Emulation -- 16. Graphical Modelling -- 17. Bayesian Hierarchical Modelling -- 18. Probabilistic Risk Analysis -- 19. Bayesian Decision Theory -- 20. Linear Modelling: LM, GLM, GAM and Mixed Models -- 21. Machine Learning -- 22. Time Series and Data Assimilation -- 23. Spatial Modelling and Scaling Error -- 24. Spatio-Temporal Modelling and Adaptive Sampling -- 25. What Next?.
Contained By:	Springer Nature eBook
標題:	Bayesian statistical decision theory. -
電子資源:	https://doi.org/10.1007/978-3-031-66085-6
ISBN:	9783031660856

Bayesian compendium
Oijen, Marcel van.

Bayesian compendium[electronic resource] /by Marcel van Oijen. - Second edition. - Cham :Springer International Publishing :2024. - xvi, 265 p. :ill. (some col.), digital ;24 cm.

- 1. Science and Uncertainty -- 2. Bayesian Inference -- 3. Assigning a Prior Distribution -- 4. Assigning a Likelihood Function -- 5. Deriving the Posterior Distribution -- 6. Markov Chain Monte Carlo Sampling (MCMC) -- 7. Sampling from the Posterior Distribution by MCMC -- 8. MCMC and Multivariate Models -- 9. Bayesian Calibration and MCMC: Frequently Asked Questions -- 10. After the Calibration: Interpretation, Reporting, Visualisation -- 11. Model Ensembles: BMC and BMA -- 12. Discrepancy -- 13. Approximations to Bayes -- 14.Thirteen Ways to Fit a Straight Line -- 15. Gaussian Processes and Model Emulation -- 16. Graphical Modelling -- 17. Bayesian Hierarchical Modelling -- 18. Probabilistic Risk Analysis -- 19. Bayesian Decision Theory -- 20. Linear Modelling: LM, GLM, GAM and Mixed Models -- 21. Machine Learning -- 22. Time Series and Data Assimilation -- 23. Spatial Modelling and Scaling Error -- 24. Spatio-Temporal Modelling and Adaptive Sampling -- 25. What Next?.

ISBN: 9783031660856

Standard No.: 10.1007/978-3-031-66085-6doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .O35 2024

Dewey Class. No.: 519.542

Bayesian compendium
LDR:02000nmm a22003375a 4500 001 2388454
003 DE-He213
005 20240828130230.0
006 m d
007 cr nn 008maaau
008 250916s2024 sz s 0 eng d
020 $a 9783031660856 $q (electronic bk.)
020 $a 9783031660849 $q (paper)
024 7 $a 10.1007/978-3-031-66085-6 $2 doi
035 $a 978-3-031-66085-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5 $b .O35 2024
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.5 $b .O39 2024
100 1 $a Oijen, Marcel van. $3 3504310
245 1 0 $a Bayesian compendium $h [electronic resource] / $c by Marcel van Oijen.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xvi, 265 p. : $b ill. (some col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
505 0 $a - 1. Science and Uncertainty -- 2. Bayesian Inference -- 3. Assigning a Prior Distribution -- 4. Assigning a Likelihood Function -- 5. Deriving the Posterior Distribution -- 6. Markov Chain Monte Carlo Sampling (MCMC) -- 7. Sampling from the Posterior Distribution by MCMC -- 8. MCMC and Multivariate Models -- 9. Bayesian Calibration and MCMC: Frequently Asked Questions -- 10. After the Calibration: Interpretation, Reporting, Visualisation -- 11. Model Ensembles: BMC and BMA -- 12. Discrepancy -- 13. Approximations to Bayes -- 14.Thirteen Ways to Fit a Straight Line -- 15. Gaussian Processes and Model Emulation -- 16. Graphical Modelling -- 17. Bayesian Hierarchical Modelling -- 18. Probabilistic Risk Analysis -- 19. Bayesian Decision Theory -- 20. Linear Modelling: LM, GLM, GAM and Mixed Models -- 21. Machine Learning -- 22. Time Series and Data Assimilation -- 23. Spatial Modelling and Scaling Error -- 24. Spatio-Temporal Modelling and Adaptive Sampling -- 25. What Next?.
650 0 $a Bayesian statistical decision theory. $3 551404
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Bayesian Inference. $3 3386929
650 2 4 $a Biostatistics. $3 1002712
650 2 4 $a Ecology. $3 516476
650 2 4 $a Environmental Monitoring. $3 770902
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-66085-6
950 $a Mathematics and Statistics (SpringerNature-11649)