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).
|Author||Minsuk Shin and Ruoxuan Tian|
|Maintainer||Minsuk Shin <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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