Journal article
Multivariate Normal Slice Sampling
Journal of computational and graphical statistics, v 19(2)
01 Jun 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
By introducing auxiliary variables, the traditional Markov chain Monte Carlo method can be improved in certain cases by implementing a "slice sampler." In the current literature, this sampling technique is used to sample from multivariate distributions with both single and multiple auxiliary variables. When the latter is employed, it generally updates one component at a time.
In this article, we propose two variations of a new multivariate normal slice sampling method that uses multiple auxiliary variables to perform multivariate updating. These methods are flexible enough to allow for truncation to a rectangular region and/or exclusion of any n-dimensional hyper-quadrant. We present results of our methods and existing state-of-the-art slice samplers by comparing efficiency and accuracy. We find that we can generate approximately iid samples at a rate that is more efficient than other methods that update all dimensions at once. Supplemental materials are available online.
Metrics
Details
- Title
- Multivariate Normal Slice Sampling
- Creators
- Merrill W. Liechty - Drexel UniversityJingjing Lu - Drexel Univ, Dept Decis Sci, LeBow Coll Business, Philadelphia, PA 19104 USA
- Publication Details
- Journal of computational and graphical statistics, v 19(2)
- Publisher
- Amer Statistical Assoc
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000279183700003
- Scopus ID
- 2-s2.0-77956653220
- Other Identifier
- 991019168981504721
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InCites Highlights
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- Web of Science research areas
- Statistics & Probability