A simple and efficient Bayesian procedure for selecting dimensionality in multidimensional scaling

The proposed method is fully Bayesian. It takes parameter estimation error into account when computing penalties for complex models and provides an uncertainty measure for the choice of dimensionality. Also, the MCMC algorithm is computationally very efficient since it visits various dimensional models in one MCMC procedure. A simulation study compares the proposed method with the Bayesian method of Oh and Raftery (2001). Three real data sets are analysed by using the proposed method.