In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities).
| Version: | 1.31 |
| Depends: | R (≥ 3.3) |
| Imports: | Matrix, stats, snowfall, abind |
| Published: | 2018-10-26 |
| Author: | Minsuk Shin and Ruoxuan Tian |
| Maintainer: | Minsuk Shin <minsuk000 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://arxiv.org/pdf/1507.07106.pdf |
| NeedsCompilation: | no |
| CRAN checks: | BayesS5 results |
| Reference manual: | BayesS5.pdf |
| Package source: | BayesS5_1.31.tar.gz |
| Windows binaries: | r-devel: BayesS5_1.31.zip, r-release: BayesS5_1.31.zip, r-oldrel: BayesS5_1.31.zip |
| OS X binaries: | r-release: BayesS5_1.31.tgz, r-oldrel: BayesS5_1.31.tgz |
| Old sources: | BayesS5 archive |
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