Bayesian statistics social scientists (35 Ergebnisse)

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Hardcover. Zustand: Very Good+. Covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. 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. Professional book seller with storefront since 1975. All orders carefully packaged and promptly shipped.…

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Hardcover. Zustand: Good. 357 Pages With The Index. Textbook Binding. Creased Pages, Used Book,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.…

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Taschenbuch. Zustand: 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.…

Zustand: New. pp. 388.

Taschenbuch. Zustand: Neu. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists | Scott M. Lynch | Taschenbuch | Statistics for Social and Behavioral Sciences | xxviii | Englisch | 2010 | Springer | EAN 9781441924346 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Buch. Zustand: 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.…

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Taschenbuch. Zustand: 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.…

Zustand: 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. .…

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Zustand: New. Print on Demand pp. 388 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 388 pp. Englisch.…