Bayesian analysis with Excel and R:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston
Pearson Education
[2023]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvi, 169 Seiten Illustrationen |
| ISBN: | 9780137580989 |
Staff View
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| adam_text | Contents Preface..................................... ix 1 Bayesian Analysis and R: An Overview.................................................................................... 1 Bayes Comes Back........................................................................................................................... 2 About Structuring Priors................................................................................................................... 4 Watching the Jargon........................................................................................................................ 4 Priors, Likelihoods, and Posteriors........................................................................................................ 8 The Prior................................................................................................................................. 8 The Likelihood.......................................................................................................................... 9 Contrasting a Frequentisi Analysis with a Bayesian................................................................................... 10 The Frequentisi Approach............................................................................................................ 11 The Bayesian Approach............................................................................................................... 12 Summary................................................................................................................................... 13 2 Generating
Posterior Distributions with the Binomial Distribution........................................... 15 Understanding the Binomial Distribution............................................................................................... 16 Understanding Some Related Functions................................................................................................ 20 Working with R s Binomial Functions.................................................................................................... 22 Using R s dbinom Function......................................................................................................... 22 Using R s pbinom Function......................................................................................................... 24 Using R s qbinom Function......................................................................................................... 25 Using R s rbinom Function......................................................................................................... 28 Grappling with the Math................................................................................................................ 28 Summary................................................................................................................................... 31 3 Understanding the Beta Distribution................................................................................... 33 Establishing the Beta Distribution in Excel.............................................................................................. 34
Comparing the Beta Distribution with the Binomial Distribution.................................................................... 35 Decoding Excel s Help Documentation for beta . dist........................................................... 42 Replicating the Analysis in R.............................................................................................................. 43 Understanding dbeta.............................................................................................................. 43 Understanding pbeta.............................................................................................................. 45 Understanding qbeta................................................................................. ............................. 47 About Confidence Intervals.......................................................................................................... 48 Applying qbeta to Confidence Intervals.......................................................................................... 50 Applying beta, iNV to Confidence Intervals.................................................................................... 50 Summary................................................................................................................................... 51 4 Grid Approximation and the Beta Distribution.............. ........................................................ 53 More on Grid Approximation............................................................................................................. 53 Setting
the Prior...................................................................................................................... 54 Using the Results of the Beta Function.................................................................. 55 Tracking the Shape and Location of the Distribution.................................................................................. 56
vi Bayesian Analysis with Excel and R Inventorying the Necessary Functions.................................................................................................. 57 Looking Behind the Curtains........................................................................................................ 61 Moving from the Underlying Formulas to the Functions............................................................................... 70 Comparing Built-in Functions with Underlying Formulas............................................................................. 72 Understanding Conjugate Priors......................................................................................................... 73 Summary................................................................................................................................... 74 5 Grid Approximation with Multiple Parameters....................................................................... 75 Setting the Stage.......................................................................................................................... 76 Global Options......................................................................................................................... 77 Local Variables......................................................................................................................... 77 Specifying the Order of Execution................................................................................................... 77 Normal Curves, Mu and
Sigma....................................................................................................... 78 Visualizing the Arrays................................................................................................................ 80 Combining Mu and Sigma........................................................................................................... 82 Putting the Data Together................................................................................................................ 82 Calculating the Probabilities......................................................................................................... 84 Folding in the Prior................................................................................................................... 86 Inventorying the Results.............................................................................................................. 88 Viewing the Results from Different Perspectives.................................................................................. 88 Summary................................................................................................................................... 95 6 Regression Using Bayesian Methods..................................................................................... 97 Regression à la Bayes...................................................................................................................... 97 Sample Regression
Analysis.............................................................................................................. 99 Matrix Algebra Methods.................................................. .............................................................. 101 Understanding quap.................................................................................................................... 103 Continuing the Code..................................................................................................................... 105 A Full Example............................................................................................................................ 106 Designing the Multiple Regression..................................................................................................... 108 Arranging a Bayesian Multiple Regression............................................................................................. 109 Summary.................................................................................................................................. 113 7 Handling Nominal Variables.............................................................................................. 115 Using Dummy Coding................................................................................................................... 117 Supplying Text Labels in Place of Codes................................................................................................ 122 Comparing Group
Means................................................................................................................ 128 Summary.................................................................................................................................. 131 8 MCMC Sampling Methods................................................................................................. 133 Quick Review of Bayesian Sampling................................................................................................... 133 Grid Approximation................................................................................................................. 134 Quadratic Approximation........................................................................................................... 136 MCMC Gets Up To Speed............................................................................................................ 139
Contents ! vii A Sample МСМС Analysis................................................................................................................. 139 ulam s Output....................................................................................................................... 143 Validating the Results................................................................................................................ 144 Getting Trace Plot Charts............................................................................................................ 146 Summary and Concluding Thoughts.................................................................................................... 147 Appendix Installation Instructions for RStan and the rethinking Package on the Windows Platform.............................................................................................. 149 Glossary............................................................................. 151 Index........................................................................................................................................ 161 Downloadable Bonus Content Excel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) To access bonus materials, please register your book at informit.com/reglster and enter ISBN 9780137580989.
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| adam_txt |
Contents Preface. ix 1 Bayesian Analysis and R: An Overview. 1 Bayes Comes Back. 2 About Structuring Priors. 4 Watching the Jargon. 4 Priors, Likelihoods, and Posteriors. 8 The Prior. 8 The Likelihood. 9 Contrasting a Frequentisi Analysis with a Bayesian. 10 The Frequentisi Approach. 11 The Bayesian Approach. 12 Summary. 13 2 Generating
Posterior Distributions with the Binomial Distribution. 15 Understanding the Binomial Distribution. 16 Understanding Some Related Functions. 20 Working with R's Binomial Functions. 22 Using R's dbinom Function. 22 Using R's pbinom Function. 24 Using R's qbinom Function. 25 Using R's rbinom Function. 28 Grappling with the Math. 28 Summary. 31 3 Understanding the Beta Distribution. 33 Establishing the Beta Distribution in Excel. 34
Comparing the Beta Distribution with the Binomial Distribution. 35 Decoding Excel's Help Documentation for beta . dist. 42 Replicating the Analysis in R. 43 Understanding dbeta. 43 Understanding pbeta. 45 Understanding qbeta. . 47 About Confidence Intervals. 48 Applying qbeta to Confidence Intervals. 50 Applying beta, iNV to Confidence Intervals. 50 Summary. 51 4 Grid Approximation and the Beta Distribution. . 53 More on Grid Approximation. 53 Setting
the Prior. 54 Using the Results of the Beta Function. 55 Tracking the Shape and Location of the Distribution. 56
vi Bayesian Analysis with Excel and R Inventorying the Necessary Functions. 57 Looking Behind the Curtains. 61 Moving from the Underlying Formulas to the Functions. 70 Comparing Built-in Functions with Underlying Formulas. 72 Understanding Conjugate Priors. 73 Summary. 74 5 Grid Approximation with Multiple Parameters. 75 Setting the Stage. 76 Global Options. 77 Local Variables. 77 Specifying the Order of Execution. 77 Normal Curves, Mu and
Sigma. 78 Visualizing the Arrays. 80 Combining Mu and Sigma. 82 Putting the Data Together. 82 Calculating the Probabilities. 84 Folding in the Prior. 86 Inventorying the Results. 88 Viewing the Results from Different Perspectives. 88 Summary. 95 6 Regression Using Bayesian Methods. 97 Regression à la Bayes. 97 Sample Regression
Analysis. 99 Matrix Algebra Methods. . 101 Understanding quap. 103 Continuing the Code. 105 A Full Example. 106 Designing the Multiple Regression. 108 Arranging a Bayesian Multiple Regression. 109 Summary. 113 7 Handling Nominal Variables. 115 Using Dummy Coding. 117 Supplying Text Labels in Place of Codes. 122 Comparing Group
Means. 128 Summary. 131 8 MCMC Sampling Methods. 133 Quick Review of Bayesian Sampling. 133 Grid Approximation. 134 Quadratic Approximation. 136 MCMC Gets Up To Speed. 139
Contents ! vii A Sample МСМС Analysis. 139 ulam's Output. 143 Validating the Results. 144 Getting Trace Plot Charts. 146 Summary and Concluding Thoughts. 147 Appendix Installation Instructions for RStan and the rethinking Package on the Windows Platform. 149 Glossary. 151 Index. 161 Downloadable Bonus Content Excel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) To access bonus materials, please register your book at informit.com/reglster and enter ISBN 9780137580989. |
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| spelling | Carlberg, Conrad George 1948- Verfasser (DE-588)129577006 aut Bayesian analysis with Excel and R Conrad Carlberg Boston Pearson Education [2023] xvi, 169 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier EXCEL (DE-588)4138932-3 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 s Wahrscheinlichkeitsverteilung (DE-588)4121894-2 s R Programm (DE-588)4705956-4 s EXCEL (DE-588)4138932-3 s b DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033995781&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Carlberg, Conrad George 1948- Bayesian analysis with Excel and R EXCEL (DE-588)4138932-3 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd R Programm (DE-588)4705956-4 gnd |
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| title | Bayesian analysis with Excel and R |
| title_auth | Bayesian analysis with Excel and R |
| title_exact_search | Bayesian analysis with Excel and R |
| title_exact_search_txtP | Bayesian analysis with Excel and R |
| title_full | Bayesian analysis with Excel and R Conrad Carlberg |
| title_fullStr | Bayesian analysis with Excel and R Conrad Carlberg |
| title_full_unstemmed | Bayesian analysis with Excel and R Conrad Carlberg |
| title_short | Bayesian analysis with Excel and R |
| title_sort | bayesian analysis with excel and r |
| topic | EXCEL (DE-588)4138932-3 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | EXCEL Bayes-Verfahren Wahrscheinlichkeitsverteilung R Programm |
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