Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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The work Introduction to Applied Bayesian Statistics and Estimation for Social Scientists represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Resource Information
The work Introduction to Applied Bayesian Statistics and Estimation for Social Scientists represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
 Statement of responsibility
 edited by Scott M. Lynch
 Language

 eng
 eng
 Summary
 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 realdata 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 MetropolisHastings 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. Scott M. Lynch is an associate professor in the Department of Sociology and Office of Population Research at Princeton University. His substantive research interests are in changes in racial and socioeconomic inequalities in health and mortality across age and time. His methodological interests are in the use of Bayesian stastistics in sociology and demography generally and in multistate life table methodology specifically
 Dewey number
 300.727
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt
 yxcQy2K0D8s
 Language note
 English
 LC call number
 H6161.95
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Statistics for Social and Behavioral Sciences,
Context
Context of Introduction to Applied Bayesian Statistics and Estimation for Social ScientistsWork of
 Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, edited by Scott M. Lynch, (electronic resource)
 Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, edited by Scott M. Lynch, (electronic resource)
 Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, edited by Scott M. Lynch, (electronic resource)
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