dataset · Figshare · 2014
Automated Factor Slice Sampling
Tibbits, Matthew M., Groendyke, Chris, Murali Haran, Liechty, John C.
Cited by 0Open access
Abstract
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. This article has online supplementary materials.
Cite this paper
Tibbits, M. M., Groendyke, C., Haran, M., & Liechty, J. C. (2014). Automated Factor Slice Sampling. Figshare. https://doi.org/10.6084/m9.figshare.1008430.v1
Tibbits, Matthew M., et al. “Automated Factor Slice Sampling.” Figshare, 2014, https://doi.org/10.6084/m9.figshare.1008430.v1.
Tibbits, Matthew M., Chris Groendyke, Murali Haran, and John C. Liechty. 2014. “Automated Factor Slice Sampling.” Figshare. https://doi.org/10.6084/m9.figshare.1008430.v1.
Tibbits, M.M. et al. (2014) ‘Automated Factor Slice Sampling’, Figshare. Available at: https://doi.org/10.6084/m9.figshare.1008430.v1.
@misc{tibbits2014automated,
title = {{Automated Factor Slice Sampling}},
author = {Tibbits, Matthew M. and Groendyke, Chris and Haran, Murali and Liechty, John C.},
journal = {Figshare},
year = {2014},
doi = {10.6084/m9.figshare.1008430.v1}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.