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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). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
| Version: | 1.41 |
| Depends: | R (≥ 3.4.0) |
| Imports: | Matrix, stats, snowfall, abind, splines2 |
| Published: | 2020-03-24 |
| DOI: | 10.32614/CRAN.package.BayesS5 |
| 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/abs/1507.07106v4 |
| NeedsCompilation: | no |
| CRAN checks: | BayesS5 results |
| Reference manual: | BayesS5.pdf |
| Package source: | BayesS5_1.41.tar.gz |
| Windows binaries: | r-devel: BayesS5_1.41.zip, r-release: BayesS5_1.41.zip, r-oldrel: BayesS5_1.41.zip |
| macOS binaries: | r-release (arm64): BayesS5_1.41.tgz, r-oldrel (arm64): BayesS5_1.41.tgz, r-release (x86_64): BayesS5_1.41.tgz, r-oldrel (x86_64): BayesS5_1.41.tgz |
| Old sources: | BayesS5 archive |
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