Automated Factor Slice Sampling
Furkejuvvon:
| Publikašuvnnas: | Journal of Computational and Graphical Statistics vol. 23, no. 2 (2014), p. 543 |
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| Váldodahkki: | |
| Eará dahkkit: | , , |
| Almmustuhtton: |
Taylor & Francis Ltd.
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract |
| Fáddágilkorat: |
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MARC
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| 001 | 1523935349 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1061-8600 | ||
| 022 | |a 1537-2715 | ||
| 035 | |a 1523935349 | ||
| 045 | 2 | |b d20140601 |b d20140831 | |
| 084 | |a 66303 |2 nlm | ||
| 100 | 1 | |a Tibbits, Matthew M | |
| 245 | 1 | |a Automated Factor Slice Sampling | |
| 260 | |b Taylor & Francis Ltd. |c 2014 | ||
| 513 | |a Feature | ||
| 520 | 3 | |a Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate distribution. We describe a two-pronged approach for constructing efficient, automated MCMC algorithms: (1) we propose the "factor slice sampler," a generalization of the univariate slice sampler where we treat the selection of a coordinate basis (factors) as an additional tuning parameter, and (2) we develop an approach for automatically selecting tuning parameters to construct an efficient factor slice sampler. In addition to automating the factor slice sampler, our tuning approach also applies to the standard univariate slice samplers. We demonstrate the efficiency and general applicability of our automated MCMC algorithm with a number of illustrative examples. [PUBLICATION ABSTRACT] | |
| 653 | |a Markov analysis | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Studies | ||
| 653 | |a Algorithms | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Probability distribution | ||
| 653 | |a Automation | ||
| 700 | 1 | |a Groendyke, Chris | |
| 700 | 1 | |a Haran, Murali | |
| 700 | 1 | |a Liechty, John C | |
| 773 | 0 | |t Journal of Computational and Graphical Statistics |g vol. 23, no. 2 (2014), p. 543 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1523935349/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |