Crandore Hub

BayesS5

Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

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.

Versions across snapshots

VersionRepositoryFileSize
1.41 rolling linux/jammy R-4.5 BayesS5_1.41.tar.gz 154.1 KiB
1.41 rolling linux/noble R-4.5 BayesS5_1.41.tar.gz 154.4 KiB
1.41 rolling source/ R- BayesS5_1.41.tar.gz 20.3 KiB
1.41 latest linux/jammy R-4.5 BayesS5_1.41.tar.gz 154.1 KiB
1.41 latest linux/noble R-4.5 BayesS5_1.41.tar.gz 154.4 KiB
1.41 latest source/ R- BayesS5_1.41.tar.gz 20.3 KiB
1.41 2026-04-26 source/ R- BayesS5_1.41.tar.gz 20.3 KiB
1.41 2026-04-23 source/ R- BayesS5_1.41.tar.gz 20.3 KiB
1.41 2026-04-09 windows/windows R-4.5 BayesS5_1.41.zip 156.9 KiB
1.41 2025-04-20 source/ R- BayesS5_1.41.tar.gz 20.3 KiB

Dependencies (latest)

Imports