Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial....
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| Veröffentlicht in: | Journal of the American Statistical Association 1990-12, Vol.85 (412), p.972-985 |
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| container_end_page | 985 |
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| container_issue | 412 |
| container_start_page | 972 |
| container_title | Journal of the American Statistical Association |
| container_volume | 85 |
| creator | Gelfand, Alan E. Hills, Susan E. Racine-Poon, Amy Smith, Adrian F. M. |
| description | The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries. |
| doi_str_mv | 10.1080/01621459.1990.10474968 |
| format | Article |
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In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries.</description><subject>Applications and Case Studies</subject><subject>Bayesian inference</subject><subject>Data models</subject><subject>Data sampling</subject><subject>Datasets</subject><subject>Density estimation</subject><subject>Exact sciences and technology</subject><subject>Hierarchical models</subject><subject>Inference</subject><subject>Marginalization</subject><subject>Mathematics</subject><subject>Missing data</subject><subject>Nonlinear parameters</subject><subject>Order-restricted inference</subject><subject>Population growth rate</subject><subject>Probability and statistics</subject><subject>Sampling</subject><subject>Sampling distributions</subject><subject>Sampling theory, sample surveys</subject><subject>Sciences and techniques of general use</subject><subject>Statistical variance</subject><subject>Statistics</subject><subject>Variance components</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1990</creationdate><recordtype>article</recordtype><recordid>eNqFkE1P3DAQhi3USmxp_wLyAfUW8LfjI6V8rAT00CJxsyaOg4y89tbOCu2_J9GyiBu-jGw9847nQeiYklNKWnJGqGJUSHNKjZmfhBZGtQdoQSXXDdPi8QtazFAzU4foW63PZDq6bRfobhnjpo4FxpATzgP-BVtfAyS8TIMvPjmPQ8L3uawg4t8wAr7LvY8VP9SQnvB16LqK_8JqHafrd_R1gFj9j7d6hB6uLv9d3DS3f66XF-e3jRNUjI3pe2K4lt5x1xPuVCeoBmCsAw5UGe2JH4iWQwu9Zkp5paUznWO-l8YoyY_Qz13uuuT_G19HuwrV-Rgh-byplmvSKqn4BKod6EqutfjBrktYQdlaSuxsz-7t2dme3dubGk_eJkB1EIcCyYX63i0FpXLa4R17rmMuH8MZJ9oy1hppxISd77CQhtnkSy6xtyNsYy77aP7Jj14BCmyOfw</recordid><startdate>19901201</startdate><enddate>19901201</enddate><creator>Gelfand, Alan E.</creator><creator>Hills, Susan E.</creator><creator>Racine-Poon, Amy</creator><creator>Smith, Adrian F. 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M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1990-12-01</date><risdate>1990</risdate><volume>85</volume><issue>412</issue><spage>972</spage><epage>985</epage><pages>972-985</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><coden>JSTNAL</coden><abstract>The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. 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| fulltext | fulltext |
| identifier | ISSN: 0162-1459 |
| ispartof | Journal of the American Statistical Association, 1990-12, Vol.85 (412), p.972-985 |
| issn | 0162-1459 1537-274X |
| language | eng |
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| source | JSTOR Mathematics & Statistics; Jstor Complete Legacy |
| subjects | Applications and Case Studies Bayesian inference Data models Data sampling Datasets Density estimation Exact sciences and technology Hierarchical models Inference Marginalization Mathematics Missing data Nonlinear parameters Order-restricted inference Population growth rate Probability and statistics Sampling Sampling distributions Sampling theory, sample surveys Sciences and techniques of general use Statistical variance Statistics Variance components |
| title | Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling |
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