MARC Record from harvard_bibliographic_metadata

LEADER: 03336cam a2200349 a 4500
001 013335669-8
005 20141001153614.0
008 100804s2011 maua b 001 0 eng
010 $a 2010030206
020 $a9780123814852 (hardcover : alk. paper)
020 $a0123814855
035 0 $aocn653121532
040 $aDLC$beng$cDLC$dYDX$dYDXCP$dCDX$dTTS$dBDX$dOCLCF$dZLM$dCHVBK
050 00 $aQA279.5$b.K79 2011
082 00 $a519.5/42$222
100 1 $aKruschke, John K.
245 10 $aDoing Bayesian data analysis :$ba tutorial with R and BUGS /$cJohn K. Kruschke.
260 $aBurlington, MA :$bAcademic Press,$cc2011.
300 $axvii, 653 p. :$bill. ;$c25 cm.
504 $aIncludes bibliographical references and index.
505 0 $aThis book's organization : read me first! -- Introduction : models we believe in -- What is this stuff called probability? -- Bayes' rule -- Inferring a binomial proportion via exact mathematical analysis -- Inferring a binomial proportion via grid approximation -- Inferring a binomial proportion via the Metropolis algorithm -- Inferring two binomial proportions via Gibbs sampling -- Bernoulli likelihood with hierarchical prior -- Hierarchical modeling and model comparison -- Null hypothesis significance testing -- Bayesian approaches to testing a point ("null") hypothesis -- Goals, power, and sample size -- Overview of the generalized linear model -- Metric predicted variable on a single group -- Metric predicted variable with one metric predictor -- Metric predicted variable with multiple metric predictors -- Metric predicted variable with one nominal predictor -- Metric predicted variable with multiple nominal predictors -- Dichotomous predicted variable -- Ordinal predicted variable -- Contingency table analysis -- Tools in the trunk.
520 $a"There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and a rustya calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods." - Publisher's description.
650 0 $aBayesian statistical decision theory.
650 0 $aR (Computer program language)
650 7 $aBayesian statistical decision theory.$2fast
650 7 $aR (Computer program language)$2fast
650 7 $aBayes-Verfahren.$2gnd
650 7 $aR.$2gnd
899 $a415_565750
988 $a20120824
049 $aHLSS
906 $0DLC