Applied bayesian statistics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC
SAGE
[2023]
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| Series: | Quantitative applications in the social sciences
191 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxi, 187 Seiten Illustrationen, Diagramme |
| ISBN: | 9781544334639 |
Staff View
MARC
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Record in the Search Index
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| adam_text | CONTENTS List of Tables xi List of Figures xiii About the Author xvii Series Editor’s Introduction xix Acknowledgments xxi 1. Introduction Data and Topics Explored in Examples Mathematical Knowledge Required for this Volume Layout of the Book 1 4 9 11 2. Probability Distributions and Review of Classical Analysis Probability Distributions Important Distributions in Bayesian Statistics Marginal and Conditional Distributions Review of Maximum Likelihood Analysis 13 15 17 25 28 3. The Bayesian Approach to Probability andStatistics Including a Prior Distribution and Summarizing the Posterior More on Priors Extending the Beta/Binomial Model to the DirichletZMultinomial Normal Distribution Examples 34 37 43 48 51 4. Markov Chain Monte Carlo (MCMC) Sampling Methods Logic of Sampling to Summarize Distributions A Simple Method for When Direct Simulation Is Impossible Markov Processes and Chains Basic Markov Chain Monte Carlo Methods Slice Sampling Evaluating MCMC Algorithm Performance 62 63 66 68 71 91 96 5. Implementing the Bayesian Approach in Realistic Applications 111 The Linear Model 111 The Dichotomous Probit Model 123 A Latent Class (Finite Mixture) Model 133 Comparing Models and Evaluating Model Fit 150
6. Conclusion Why Take a Bayesian Perspective? Some Additional Suggested Readings 160 161 166 Appendix 1. R Program for Simple Three-State Model With Uniform Proposal in Chapter 4 2. R Program for Simple Three-State Model With Nonuniform Proposal in Chapter 4 3. Random Walk Metropolis-Hastings Algorithm 4. Function for Computing Original Gelman-Rubin Diagnostic 5. Gibbs Sampler for the Linear Regression Model With Reference Prior 6. Gibbs Sampler for the Probit Regression Model With Uniform Priors 7. Gibbs Sampler for Two-Class Latent Class Model With Priors 8. Posterior Predictive Distribution Simulation (for Linear Model) 169 169 170 171 172 173 174 175 177 References 178 Index 181
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| adam_txt |
CONTENTS List of Tables xi List of Figures xiii About the Author xvii Series Editor’s Introduction xix Acknowledgments xxi 1. Introduction Data and Topics Explored in Examples Mathematical Knowledge Required for this Volume Layout of the Book 1 4 9 11 2. Probability Distributions and Review of Classical Analysis Probability Distributions Important Distributions in Bayesian Statistics Marginal and Conditional Distributions Review of Maximum Likelihood Analysis 13 15 17 25 28 3. The Bayesian Approach to Probability andStatistics Including a Prior Distribution and Summarizing the Posterior More on Priors Extending the Beta/Binomial Model to the DirichletZMultinomial Normal Distribution Examples 34 37 43 48 51 4. Markov Chain Monte Carlo (MCMC) Sampling Methods Logic of Sampling to Summarize Distributions A Simple Method for When Direct Simulation Is Impossible Markov Processes and Chains Basic Markov Chain Monte Carlo Methods Slice Sampling Evaluating MCMC Algorithm Performance 62 63 66 68 71 91 96 5. Implementing the Bayesian Approach in Realistic Applications 111 The Linear Model 111 The Dichotomous Probit Model 123 A Latent Class (Finite Mixture) Model 133 Comparing Models and Evaluating Model Fit 150
6. Conclusion Why Take a Bayesian Perspective? Some Additional Suggested Readings 160 161 166 Appendix 1. R Program for Simple Three-State Model With Uniform Proposal in Chapter 4 2. R Program for Simple Three-State Model With Nonuniform Proposal in Chapter 4 3. Random Walk Metropolis-Hastings Algorithm 4. Function for Computing Original Gelman-Rubin Diagnostic 5. Gibbs Sampler for the Linear Regression Model With Reference Prior 6. Gibbs Sampler for the Probit Regression Model With Uniform Priors 7. Gibbs Sampler for Two-Class Latent Class Model With Priors 8. Posterior Predictive Distribution Simulation (for Linear Model) 169 169 170 171 172 173 174 175 177 References 178 Index 181 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T20:07:49Z |
| indexdate | 2024-07-10T09:34:45Z |
| institution | BVB |
| isbn | 9781544334639 |
| language | English |
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| physical | xxi, 187 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | SAGE |
| record_format | marc |
| series | Quantitative applications in the social sciences |
| series2 | Quantitative applications in the social sciences |
| spelling | Lynch, Scott Michael 1971- Verfasser (DE-588)17395359X aut Applied bayesian statistics Scott M. Lynch, Duke University Los Angeles ; London ; New Delhi ; Singapore ; Washington DC SAGE [2023] © 2023 xxi, 187 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Quantitative applications in the social sciences 191 Soziologe (DE-588)4055930-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Soziologe (DE-588)4055930-0 s Statistik (DE-588)4056995-0 s Bayes-Verfahren (DE-588)4204326-8 s DE-604 Quantitative applications in the social sciences 191 (DE-604)BV000005102 191 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=033684705&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lynch, Scott Michael 1971- Applied bayesian statistics Quantitative applications in the social sciences Soziologe (DE-588)4055930-0 gnd Statistik (DE-588)4056995-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4055930-0 (DE-588)4056995-0 (DE-588)4204326-8 |
| title | Applied bayesian statistics |
| title_auth | Applied bayesian statistics |
| title_exact_search | Applied bayesian statistics |
| title_exact_search_txtP | Applied bayesian statistics |
| title_full | Applied bayesian statistics Scott M. Lynch, Duke University |
| title_fullStr | Applied bayesian statistics Scott M. Lynch, Duke University |
| title_full_unstemmed | Applied bayesian statistics Scott M. Lynch, Duke University |
| title_short | Applied bayesian statistics |
| title_sort | applied bayesian statistics |
| topic | Soziologe (DE-588)4055930-0 gnd Statistik (DE-588)4056995-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Soziologe Statistik Bayes-Verfahren |
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