Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models

Markov chain Monte Carlo (MCMC) implementations of Bayesian inference for latent spatial Gaussian models are very computationally intensive, and restrictions on storage and computation time are limiting their application to large problems. Here we propose various parallel MCMC algorithms for such models. The algorithms' performance is discussed with respect to a simulation study, which demonstrates the increase in speed with which the algorithms explore the posterior distribution as a function of the number of processors. We also discuss how feasible problem size is increased by use of these algorithms.     

An Empirical Investigation of the Malcolm Baldrige National Quality Award Causal Model

The objective of this research is to test the theory and causal performance linkages implied by the Malcolm Baldrige National Quality Award (MBNQA). The survey instrument used a comprehensive set of 101 questions that were directly tied to specific criteria in the 1995 MBNQA Criteria. Results reported here represent the first published article that tests the MBNQA performance relationships and causal model using comprehensive measurement and structural models. In general, our research concludes that (1) The underlying theory of the MBNQA is supported that “leadership drives the system that causes results”; (2) Leadership is the most important driver of system performance; (3) Leadership has no direct effect on Financial Results but must influence overall performance “through the system”; (4) Information and Analysis is statistically the second most important Baldrige category; (5) the Baldrige category, Process Management, is twice as important when predicting customer satisfaction as when predicting financial results; and (6) a modified “within system” set of five Baldrige causal relationships is a good predictor of organizational performance.