| Name : R-BayesS5
| |
| Version : 1.41
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.4.7
| Date : 2024-06-14 11:40:04
|
| Group : Development/Libraries/Other
| Source RPM : R-BayesS5-1.41-lp153.4.7.src.rpm
|
| Size : 0.16 MB
| |
| Packager : (none)
| |
| Summary : Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
