國立虎尾科技大學 |

Bayesian Compendium

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian Compendium / by Marcel van Oijen.
作者:	van Oijen, Marcel.
面頁冊數:	XIV, 204 p. 60 illus., 23 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Analytical Chemistry. -
電子資源:	https://doi.org/10.1007/978-3-030-55897-0
ISBN:	9783030558970

Bayesian Compendium
van Oijen, Marcel.

Bayesian Compendium [electronic resource] /by Marcel van Oijen. - 1st ed. 2020. - XIV, 204 p. 60 illus., 23 illus. in color.online resource.

Preface -- 1 Introduction to Bayesian thinking -- 2 Introduction to Bayesian science -- 3 Assigning a prior distribution -- 4 Assigning a likelihood function -- 5 Deriving the posterior distribution -- 6 Sampling from any distribution by MCMC -- 7 Sampling from the posterior distribution by MCMC -- 8 Twelve ways to fit a straight line -- 9 MCMC and complex models -- 10 Bayesian calibration and MCMC: Frequently asked questions -- 11 After the calibration: Interpretation, reporting, visualization -- 2 Model ensembles: BMC and BMA -- 13 Discrepancy -- 14 Gaussian Processes and model emulation -- 15 Graphical Modelling (GM) -- 16 Bayesian Hierarchical Modelling (BHM) -- 17 Probabilistic risk analysis and Bayesian decision theory -- 18 Approximations to Bayes -- 19 Linear modelling: LM, GLM, GAM and mixed models -- 20 Machine learning -- 21 Time series and data assimilation -- 22 Spatial modelling and scaling error -- 23 Spatio-temporal modelling and adaptive sampling -- 24 What next? -- Appendix 1: Notation and abbreviations -- Appendix 2: Mathematics for modellers -- Appendix 3: Probability theory for modellers -- Appendix 4: R -- Appendix 5: Bayesian software.

This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn’t difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion). This book provides a short and easy guide to all of these and more. It was written from a unifying Bayesian perspective, which reveals how the multitude of techniques and approaches are in fact all related to one another. Basic notions from probability theory are introduced. Executable code examples are included to enhance the book’s practical use for scientific modellers, and all code is available online as well.

ISBN: 9783030558970

Standard No.: 10.1007/978-3-030-55897-0doiSubjects--Topical Terms:

668584
Analytical Chemistry.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Bayesian Compendium
LDR:03536nam a22003975i 4500 001 1029284
003 DE-He213
005 20200917133108.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030558970 $9 978-3-030-55897-0
024 7 $a 10.1007/978-3-030-55897-0 $2 doi
035 $a 978-3-030-55897-0
050 4 $a QA276-280
072 7 $a PBTB $2 bicssc
072 7 $a MAT029010 $2 bisacsh
072 7 $a PBTB $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a van Oijen, Marcel. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325937
245 1 0 $a Bayesian Compendium $h [electronic resource] / $c by Marcel van Oijen.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIV, 204 p. 60 illus., 23 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Preface -- 1 Introduction to Bayesian thinking -- 2 Introduction to Bayesian science -- 3 Assigning a prior distribution -- 4 Assigning a likelihood function -- 5 Deriving the posterior distribution -- 6 Sampling from any distribution by MCMC -- 7 Sampling from the posterior distribution by MCMC -- 8 Twelve ways to fit a straight line -- 9 MCMC and complex models -- 10 Bayesian calibration and MCMC: Frequently asked questions -- 11 After the calibration: Interpretation, reporting, visualization -- 2 Model ensembles: BMC and BMA -- 13 Discrepancy -- 14 Gaussian Processes and model emulation -- 15 Graphical Modelling (GM) -- 16 Bayesian Hierarchical Modelling (BHM) -- 17 Probabilistic risk analysis and Bayesian decision theory -- 18 Approximations to Bayes -- 19 Linear modelling: LM, GLM, GAM and mixed models -- 20 Machine learning -- 21 Time series and data assimilation -- 22 Spatial modelling and scaling error -- 23 Spatio-temporal modelling and adaptive sampling -- 24 What next? -- Appendix 1: Notation and abbreviations -- Appendix 2: Mathematics for modellers -- Appendix 3: Probability theory for modellers -- Appendix 4: R -- Appendix 5: Bayesian software.
520 $a This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn’t difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion). This book provides a short and easy guide to all of these and more. It was written from a unifying Bayesian perspective, which reveals how the multitude of techniques and approaches are in fact all related to one another. Basic notions from probability theory are introduced. Executable code examples are included to enhance the book’s practical use for scientific modellers, and all code is available online as well.
650 2 4 $a Analytical Chemistry. $3 668584
650 2 4 $a Monitoring/Environmental Analysis. $3 1113426
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Ecology. $3 575279
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 1 4 $a Bayesian Inference. $3 1211345
650 0 $a Analytical chemistry. $3 1182118
650 0 $a Environmental monitoring. $3 555916
650 0 $a Ecology . $3 1253481
650 0 $a Statistics . $3 1253516
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030558963
776 0 8 $i Printed edition: $z 9783030558987
776 0 8 $i Printed edition: $z 9783030558994
856 4 0 $u https://doi.org/10.1007/978-3-030-55897-0
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)