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001			978-0-387-71265-9
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007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387712659 _9978-0-387-71265-9
024	7		_a10.1007/978-0-387-71265-9 _2doi
050		4	_aH61-61.95
072		7	_aJHBC _2bicssc
072		7	_aSOC019000 _2bisacsh
082	0	4	_a300.1 _223
245	1	0	_aIntroduction to Applied Bayesian Statistics and Estimation for Social Scientists _h[electronic resource] / _cedited by Scott M. Lynch.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXXVIII, 359 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics for Social and Behavioral Sciences
505	0		_aProbability Theory and Classical Statistics -- Basics of Bayesian Statistics -- Modern Model Estimation Part 1: Gibbs Sampling -- Modern Model Estimation Part 2: Metroplis–Hastings Sampling -- Evaluating Markov Chain Monte Carlo Algorithms and Model Fit -- The Linear Regression Model -- Generalized Linear Models -- to Hierarchical Models -- to Multivariate Regression Models -- Conclusion.  .
520			_aIntroduction 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. 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.
650		0	_aSocial sciences.
650		0	_aStatistics.
650		0	_aDemography.
650	1	4	_aSocial Sciences.
650	2	4	_aMethodology of the Social Sciences.
650	2	4	_aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650	2	4	_aDemography.
700	1		_aLynch, Scott M. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387712642
830		0	_aStatistics for Social and Behavioral Sciences
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-71265-9
912			_aZDB-2-SHU
950			_aHumanities, Social Sciences and Law (Springer-11648)
999			_c502984 _d502984