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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Couverture souple
Synopsis
This book provides an introduction to the Bayesian approach to statistical analysis of data, written at a level that is accessible to a social science audience. The book covers the Bayesian approach from model development through the development and implementation of programs to estimate the model, through summation and interpretation of the output. The first part provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods—including the Gibbs sampler and the Metropolis-Hastings algorithm—are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming Markov chain Monte Carlo algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.
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Présentation de l'éditeur
This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer (edition Softcover reprint of hardcover 1st ed. 2007), 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Ancien ou d'occasion
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Paperback. Etat : Fair. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported Softcover reprint of hardcover 1st ed. 2007. N° de réf. du vendeur 1441924345-7-1
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Ancien ou d'occasion
Couverture souple
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Ancien ou d'occasion
Couverture souple
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Ancien ou d'occasion
Paperback
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Springer New York, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
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Couverture souple
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book written at an introductory level for social scientists interested in learning about MCMCThis book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. . N° de réf. du vendeur 4172948
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Edité par
Springer New York Nov 2010, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists' covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data. 388 pp. Englisch. N° de réf. du vendeur 9781441924346
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Edité par
Springer New York, Springer US Nov 2010, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists' covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 388 pp. Englisch. N° de réf. du vendeur 9781441924346
Quantité disponible : 2 disponible(s)
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Edité par
Springer New York, Springer US, 2010
ISBN 10 : 1441924345
ISBN 13 : 9781441924346
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists' covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data. N° de réf. du vendeur 9781441924346
Quantité disponible : 1 disponible(s)