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In den WarenkorbPaperback. Zustand: Brand New. 187 pages. 8.25x5.50x0.50 inches. In Stock.
Sprache: Englisch
Verlag: Sage Publications|SAGE Publications, Inc, 2022
ISBN 10: 154433463X ISBN 13: 9781544334639
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Zustand: New. Applied Bayesian Statistics provides a broad, but in-depth introduction to Bayesian statistics, both in terms of its basic theoretical underpinnings and its contemporary methods of application. The book is highly applied more of a how to .
Sprache: Englisch
Verlag: Sage Publications, SAGE Publications, Inc Nov 2022, 2022
ISBN 10: 154433463X ISBN 13: 9781544334639
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Bayesian statistical analyses have become increasingly common over the last two decades. The rapid increase in computing power that facilitated their implementation coincided with major changes in the research interests of, and data availability for, social scientists. Specifically, the last two decades have seen an increase in the availability of panel data sets, other hierarchically structured data sets including spatially organized data, along with interests in life course processes and the influence of context on individual behavior and outcomes. The Bayesian approach to statistics is well-suited for these types of data and research questions. Applied Bayesian Statistics is an introduction to these methods that is geared toward social scientists. Author Scott M. Lynch makes the material accessible by emphasizing application more than theory, explaining the math in a step-by-step fashion, and demonstrating the Bayesian approach in analyses of U.S. political trends drawing on data from the General Social Survey.
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Taschenbuch. Zustand: Neu. Applied Bayesian Statistics | Scott M. Lynch | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Sage Publications | EAN 9781544334639 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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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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In den WarenkorbPaperback. Zustand: Brand New. 364 pages. 8.80x6.00x1.10 inches. In Stock.
Sprache: Englisch
Verlag: Springer New York, Springer US, 2010
ISBN 10: 1441924345 ISBN 13: 9781441924346
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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.
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.