Automated Factor Slice Sampling
| Autor: | Matthew M. Tibbits, Murali Haran, John Liechty, Chris Groendyke |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Basis (linear algebra) Computer science Generalization Univariate Sampling (statistics) Slice sampling Markov chain Monte Carlo Multivariate normal distribution computer.software_genre Article Statistics::Computation symbols.namesake symbols Statistics::Methodology Discrete Mathematics and Combinatorics Data mining Statistics Probability and Uncertainty computer Selection (genetic algorithm) |
| Zdroj: | Journal of Computational and Graphical Statistics. 23:543-563 |
| ISSN: | 1537-2715 1061-8600 |
| Popis: | 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 in order 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. |
| Databáze: | OpenAIRE |
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