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).

Getting started

Package details

AuthorMinsuk Shin and Ruoxuan Tian
MaintainerMinsuk Shin <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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BayesS5 documentation built on May 2, 2019, 2:51 p.m.