01759     2200241   4500001002100000005001500021035002000036008004100056020001800097041000800115082000800123084001400131100002000145245010300165260003200268300004000300500002500340504001600365520106600381650001501447990003001462990002501492INLIS00000000000488820221019023849  a0010-0520004888221019                |          | eng    a9780387712642  aeng  a310  a310 LYN i0 aLynch, Scott M.1 aIntroduction to Applied Bayesian Statistics and Estimation for Social Scientists /cScott M. Lynch  aNew York :bSpringer,c2007  axxviii, 357p. :b: illus. ;c24 cm.  aIndeks : p.353 - 357  ap.345 - 351  aThis book covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. 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. It provides a detailed introduction to mathematical statistics and teh 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. 4aStatistics  a09696/MKRI/MKRI-P/XI-2008  a09695/MKRI-P/XI-2008