A More Efficient Gibbs Sampler Estimation Using Steady States Simulation: Application to Public Health StudiesStatistical Computation and Simulation
AbstractMarkov chain Monte Carlo methods, in particular, the Gibbs sampler, are widely used algorithms both in application and theoretical works in the classical and Bayesian paradigms. However, these algorithms are often computer intensive. Samawi et al. [Steady-state ranked Gibbs sampler. J. Stat. Comput. Simul. 2012;82(8), 1223–1238. doi:10.1080/00949655.2011.575378] demonstrate through theory and simulation that the dependent steady-state Gibbs sampler is more efficient and accurate in model parameter estimation than the original Gibbs sampler. This paper proposes the independent steady-state Gibbs sampler (ISSGS) approach to improve the original Gibbs sampler in multidimensional problems. It is demonstrated that ISSGS provides accuracy with unbiased estimation and improves the performance and convergence of the Gibbs sampler in multidimensional problems.
Citation InformationMartin Dunbar, Hani Samawi, Robert L. Vogel and Lili Yu. "A More Efficient Gibbs Sampler Estimation Using Steady States Simulation: Application to Public Health Studies" Statistical Computation and Simulation Vol. 84 (2014) p. 1931 - 1945
Available at: http://works.bepress.com/robert_vogel/65/