Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)
Bernoulli-Uniform model prior
Tuning parameter selection for nonlocal priors
Zellner's g-prior
the log-marginal likelhood function based on the invers moment functio...
the log-marginal likelhood function based on peMoM priors and inverse ...
the log-marginal likelhood function based on piMoM priors
the log posterior distribution based on g-priors and inverse gamma pri...
the log posterior distribution based on peMoM priors and inverse gamma...
the log posterior distribution based on piMoM priors and inverse gamma...
Posterior inference results from the object of S5
Posterior inference results from the object of S5
Posterior inference results from the object of S5
Simplified shotgun stochastic search algorithm with screening (S5)
Simplified shotgun stochastic search algorithm with screening (S5) for...
Parallel version of S5
Shotgun stochastic search algorithm (SSS)
Uniform model prior
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.