Bayesian statistics and marketing:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken
Wiley
2012
|
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | TUM01 Volltext |
| Beschreibung: | Discussion Bayesian Statistics and Marketing; Contents; 1 Introduction; 1.1 A Basic Paradigm for Marketing Problems; 1.2 A Simple Example; 1.3 Benefits and Costs of the Bayesian Approach; 1.4 An Overview of Methodological Material and Case Studies; 1.5 Computing and This Book; Acknowledgements; 2 Bayesian Essentials; 2.0 Essential Concepts from Distribution Theory; 2.1 The Goal of Inference and Bayes' Theorem; 2.2 Conditioning and the Likelihood Principle; 2.3 Prediction and Bayes; 2.4 Summarizing the Posterior; 2.5 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 2.6 Identification and Bayesian Inference2.7 Conjugacy, Sufficiency, and Exponential Families; 2.8 Regression and Multivariate Analysis Examples; 2.9 Integration and Asymptotic Methods; 2.10 Importance Sampling; 2.11 Simulation Primer for Bayesian Problems; 2.12 Simulation from the Posterior of the Multivariate Regression Model; 3 Markov Chain Monte Carlo Methods; 3.1 Markov Chain Monte Carlo Methods; 3.2 A Simple Example: Bivariate Normal Gibbs Sampler; 3.3 Some Markov Chain Theory; 3.4 Gibbs Sampler; 3.5 Gibbs Sampler for the Seemingly Unrelated Regression Model 3.6 Conditional Distributions and Directed Graphs3.7 Hierarchical Linear Models; 3.8 Data Augmentation and a Probit Example; 3.9 Mixtures of Normals; 3.10 Metropolis Algorithms; 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model; 3.12 Hybrid Markov Chain Monte Carlo Methods; 3.13 Diagnostics; 4 Unit-Level Models and Discrete Demand; 4.1 Latent Variable Models; 4.2 Multinomial Probit Model; 4.3 Multivariate Probit Model; 4.4 Demand Theory and Models Involving Discrete Choice; 5 Hierarchical Models for Heterogeneous Units; 5.1 Heterogeneity and Priors 5.2 Hierarchical Models5.3 Inference for Hierarchical Models; 5.4 A Hierarchical Multinomial Logit Example; 5.5 Using Mixtures of Normals; 5.6 Further Elaborations of the Normal Model of Heterogeneity; 5.7 Diagnostic Checks of the First-Stage Prior; 5.8 Findings and Influence on Marketing Practice; 6 Model Choice and Decision Theory; 6.1 Model Selection; 6.2 Bayes Factors in the Conjugate Setting; 6.3 Asymptotic Methods for Computing Bayes Factors; 6.4 Computing Bayes Factors Using Importance Sampling; 6.5 Bayes Factors Using MCMC Draws; 6.6 Bridge Sampling Methods 6.7 Posterior Model Probabilities with Unidentified Parameters6.8 Chib's Method; 6.9 An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models; 6.10 Marketing Decisions and Bayesian Decision Theory; 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information; 7 Simultaneity; 7.1 A Bayesian Approach to Instrumental Variables; 7.2 Structural Models and Endogeneity/Simultaneity; 7.3 Nonrandom Marketing Mix Variables; Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts; Background; Model; Data; Results The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product p |
| Beschreibung: | 1 Online-Ressource (x, 348 p.) |
| ISBN: | 0470863684 9780470863688 |
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| 500 | |a Bayesian Statistics and Marketing; Contents; 1 Introduction; 1.1 A Basic Paradigm for Marketing Problems; 1.2 A Simple Example; 1.3 Benefits and Costs of the Bayesian Approach; 1.4 An Overview of Methodological Material and Case Studies; 1.5 Computing and This Book; Acknowledgements; 2 Bayesian Essentials; 2.0 Essential Concepts from Distribution Theory; 2.1 The Goal of Inference and Bayes' Theorem; 2.2 Conditioning and the Likelihood Principle; 2.3 Prediction and Bayes; 2.4 Summarizing the Posterior; 2.5 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators | ||
| 500 | |a 2.6 Identification and Bayesian Inference2.7 Conjugacy, Sufficiency, and Exponential Families; 2.8 Regression and Multivariate Analysis Examples; 2.9 Integration and Asymptotic Methods; 2.10 Importance Sampling; 2.11 Simulation Primer for Bayesian Problems; 2.12 Simulation from the Posterior of the Multivariate Regression Model; 3 Markov Chain Monte Carlo Methods; 3.1 Markov Chain Monte Carlo Methods; 3.2 A Simple Example: Bivariate Normal Gibbs Sampler; 3.3 Some Markov Chain Theory; 3.4 Gibbs Sampler; 3.5 Gibbs Sampler for the Seemingly Unrelated Regression Model | ||
| 500 | |a 3.6 Conditional Distributions and Directed Graphs3.7 Hierarchical Linear Models; 3.8 Data Augmentation and a Probit Example; 3.9 Mixtures of Normals; 3.10 Metropolis Algorithms; 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model; 3.12 Hybrid Markov Chain Monte Carlo Methods; 3.13 Diagnostics; 4 Unit-Level Models and Discrete Demand; 4.1 Latent Variable Models; 4.2 Multinomial Probit Model; 4.3 Multivariate Probit Model; 4.4 Demand Theory and Models Involving Discrete Choice; 5 Hierarchical Models for Heterogeneous Units; 5.1 Heterogeneity and Priors | ||
| 500 | |a 5.2 Hierarchical Models5.3 Inference for Hierarchical Models; 5.4 A Hierarchical Multinomial Logit Example; 5.5 Using Mixtures of Normals; 5.6 Further Elaborations of the Normal Model of Heterogeneity; 5.7 Diagnostic Checks of the First-Stage Prior; 5.8 Findings and Influence on Marketing Practice; 6 Model Choice and Decision Theory; 6.1 Model Selection; 6.2 Bayes Factors in the Conjugate Setting; 6.3 Asymptotic Methods for Computing Bayes Factors; 6.4 Computing Bayes Factors Using Importance Sampling; 6.5 Bayes Factors Using MCMC Draws; 6.6 Bridge Sampling Methods | ||
| 500 | |a 6.7 Posterior Model Probabilities with Unidentified Parameters6.8 Chib's Method; 6.9 An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models; 6.10 Marketing Decisions and Bayesian Decision Theory; 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information; 7 Simultaneity; 7.1 A Bayesian Approach to Instrumental Variables; 7.2 Structural Models and Endogeneity/Simultaneity; 7.3 Nonrandom Marketing Mix Variables; Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts; Background; Model; Data; Results | ||
| 500 | |a The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product p | ||
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Datensatz im Suchindex
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| author | Rossi, Peter E. 1955- |
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| author_facet | Rossi, Peter E. 1955- |
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| author_sort | Rossi, Peter E. 1955- |
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| callnumber-label | HF5415 |
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| callnumber-search | HF5415.2 |
| callnumber-sort | HF 45415.2 |
| callnumber-subject | HF - Commerce |
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| dewey-full | 658.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.8 |
| dewey-search | 658.8 |
| dewey-sort | 3658.8 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV041052121 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:38:33Z |
| institution | BVB |
| isbn | 0470863684 9780470863688 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026029371 |
| oclc_num | 793995918 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (x, 348 p.) |
| psigel | ZDB-4-NLEBK ZDB-4-NLEBK TUM_PDA_EBSCO_BAE_gekauft |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Rossi, Peter E. 1955- Verfasser (DE-588)141279192 aut Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch Hoboken Wiley 2012 1 Online-Ressource (x, 348 p.) txt rdacontent c rdamedia cr rdacarrier Wiley series in probability and statistics Discussion Bayesian Statistics and Marketing; Contents; 1 Introduction; 1.1 A Basic Paradigm for Marketing Problems; 1.2 A Simple Example; 1.3 Benefits and Costs of the Bayesian Approach; 1.4 An Overview of Methodological Material and Case Studies; 1.5 Computing and This Book; Acknowledgements; 2 Bayesian Essentials; 2.0 Essential Concepts from Distribution Theory; 2.1 The Goal of Inference and Bayes' Theorem; 2.2 Conditioning and the Likelihood Principle; 2.3 Prediction and Bayes; 2.4 Summarizing the Posterior; 2.5 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 2.6 Identification and Bayesian Inference2.7 Conjugacy, Sufficiency, and Exponential Families; 2.8 Regression and Multivariate Analysis Examples; 2.9 Integration and Asymptotic Methods; 2.10 Importance Sampling; 2.11 Simulation Primer for Bayesian Problems; 2.12 Simulation from the Posterior of the Multivariate Regression Model; 3 Markov Chain Monte Carlo Methods; 3.1 Markov Chain Monte Carlo Methods; 3.2 A Simple Example: Bivariate Normal Gibbs Sampler; 3.3 Some Markov Chain Theory; 3.4 Gibbs Sampler; 3.5 Gibbs Sampler for the Seemingly Unrelated Regression Model 3.6 Conditional Distributions and Directed Graphs3.7 Hierarchical Linear Models; 3.8 Data Augmentation and a Probit Example; 3.9 Mixtures of Normals; 3.10 Metropolis Algorithms; 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model; 3.12 Hybrid Markov Chain Monte Carlo Methods; 3.13 Diagnostics; 4 Unit-Level Models and Discrete Demand; 4.1 Latent Variable Models; 4.2 Multinomial Probit Model; 4.3 Multivariate Probit Model; 4.4 Demand Theory and Models Involving Discrete Choice; 5 Hierarchical Models for Heterogeneous Units; 5.1 Heterogeneity and Priors 5.2 Hierarchical Models5.3 Inference for Hierarchical Models; 5.4 A Hierarchical Multinomial Logit Example; 5.5 Using Mixtures of Normals; 5.6 Further Elaborations of the Normal Model of Heterogeneity; 5.7 Diagnostic Checks of the First-Stage Prior; 5.8 Findings and Influence on Marketing Practice; 6 Model Choice and Decision Theory; 6.1 Model Selection; 6.2 Bayes Factors in the Conjugate Setting; 6.3 Asymptotic Methods for Computing Bayes Factors; 6.4 Computing Bayes Factors Using Importance Sampling; 6.5 Bayes Factors Using MCMC Draws; 6.6 Bridge Sampling Methods 6.7 Posterior Model Probabilities with Unidentified Parameters6.8 Chib's Method; 6.9 An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models; 6.10 Marketing Decisions and Bayesian Decision Theory; 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information; 7 Simultaneity; 7.1 A Bayesian Approach to Instrumental Variables; 7.2 Structural Models and Endogeneity/Simultaneity; 7.3 Nonrandom Marketing Mix Variables; Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts; Background; Model; Data; Results The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product p BUSINESS & ECONOMICS / Distribution bisacsh BUSINESS & ECONOMICS / Marketing / General bisacsh Mathematisches Modell Wirtschaft Marketing research / Mathematical models Marketing / Mathematical models Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Marketingforschung (DE-588)4200055-5 gnd rswk-swf Bayer-Verfahren (DE-588)4210428-2 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Marketingforschung (DE-588)4200055-5 s Marketing (DE-588)4037589-4 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Bayer-Verfahren (DE-588)4210428-2 s R Programm (DE-588)4705956-4 s 1\p DE-604 Bayes-Verfahren (DE-588)4204326-8 s 2\p DE-604 3\p DE-604 Allenby, Greg M. 1956- Sonstige (DE-588)170699765 oth McCulloch, Robert E. Sonstige (DE-588)170698955 oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=507328 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rossi, Peter E. 1955- Bayesian statistics and marketing BUSINESS & ECONOMICS / Distribution bisacsh BUSINESS & ECONOMICS / Marketing / General bisacsh Mathematisches Modell Wirtschaft Marketing research / Mathematical models Marketing / Mathematical models Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Marketing (DE-588)4037589-4 gnd Marketingforschung (DE-588)4200055-5 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd R Programm (DE-588)4705956-4 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4204326-8 (DE-588)4037589-4 (DE-588)4200055-5 (DE-588)4210428-2 (DE-588)4114528-8 (DE-588)4705956-4 (DE-588)4144220-9 |
| title | Bayesian statistics and marketing |
| title_auth | Bayesian statistics and marketing |
| title_exact_search | Bayesian statistics and marketing |
| title_full | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_fullStr | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_full_unstemmed | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_short | Bayesian statistics and marketing |
| title_sort | bayesian statistics and marketing |
| topic | BUSINESS & ECONOMICS / Distribution bisacsh BUSINESS & ECONOMICS / Marketing / General bisacsh Mathematisches Modell Wirtschaft Marketing research / Mathematical models Marketing / Mathematical models Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Marketing (DE-588)4037589-4 gnd Marketingforschung (DE-588)4200055-5 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd R Programm (DE-588)4705956-4 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | BUSINESS & ECONOMICS / Distribution BUSINESS & ECONOMICS / Marketing / General Mathematisches Modell Wirtschaft Marketing research / Mathematical models Marketing / Mathematical models Bayesian statistical decision theory Bayes-Verfahren Marketing Marketingforschung Bayer-Verfahren R Programm Bayes-Entscheidungstheorie |
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| work_keys_str_mv | AT rossipetere bayesianstatisticsandmarketing AT allenbygregm bayesianstatisticsandmarketing AT mccullochroberte bayesianstatisticsandmarketing |