Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Couverture rigide
Livre 11 sur 40: Statistics for Social and Behavioral Sciences
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Introduction to Applied Bayesi
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Springer, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
Ancien ou d'occasion
Couverture rigide
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
Ancien ou d'occasion
Couverture rigide
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)
Edité par
Springer, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
Ancien ou d'occasion
Couverture rigide
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Hardcover. Etat : Good. 357 Pages With The Index. Textbook Binding. Creased Pages, Used Book,books are NOT signed. We will state signed at the description section. we confirm they are signed via email or stated in the description box. - Specializing in academic, collectiblle and historically significant, providing the utmost quality and customer service satisfaction. For any questions feel free to email us. N° de réf. du vendeur X14785x
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Springer New York Jul 2007, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
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Buch. 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. 359 pp. Englisch. N° de réf. du vendeur 9780387712642
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Springer New York, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
Neuf
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Gebunden. Etat : New. 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 5910590
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Etat : New. Print on Demand pp. 388 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam. N° de réf. du vendeur 7594660
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Edité par
Springer New York, 2007
ISBN 10 : 038771264X
ISBN 13 : 9780387712642
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. 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 9780387712642
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