Composition algorithms for conditional distributions
| Authors |
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|---|---|
| Publication date | 2023 |
| Host editors |
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| Book title | Essays on Contemporary Psychometrics |
| ISBN |
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| ISBN (electronic) |
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| Series | Methodology of Educational Measurement and Assessment |
| Pages (from-to) | 219-250 |
| Publisher | Cham: Springer |
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| Abstract | This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from intractable full-conditional distributions of the person and item parameters in an application of the Gibbs sampler. |
| Document type | Chapter |
| Language | English |
| Published at |
https://doi.org/10.31234/osf.io/e5yjp
(Submitted manuscript)
https://doi.org/10.1007/978-3-031-10370-4_12
(Final published version)
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| Permalink to this page | |
