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 (2017+), by Minsuk Shin, Anirban Bhattachary, and Valen E. Johnson, accepted in Statistica Sinica.

Packaged: 2018-10-26 05:58:43 UTC; shinminsuk

Imports: Matrix, stats, snowfall, abind, splines2
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.

Title: Bayesian Analysis of Small Area Estimation
Author: Chengchun Shi, with contributions from Peng Zhang
Description: This package provides a variety of functions to deal with several specific small area area-
level models in Bayesian context. Models provided range from the basic Fay-Herriot model to
its improvement such as You-Chapman models, unmatched models, spatial models and so on.
Different types of priors for specific parameters could be chosen to obtain MCMC posterior
draws. The main sampling function is written in C with GSL lab so as to facilitate the
computation. Model internal checking and model comparison criteria are also involved.