Markov chain Monte Carlo based on deterministic transformations

TMCMC is compared with MH using the well-known Challenger data, demonstrating the effectiveness of the former in the case of highly correlated variables. Moreover, we apply our methodology to a challenging posterior simulation problem associated with the geostatistical model of Diggle et al. (1998)  [7], updating 160 unknown parameters jointly, using a deterministic transformation of a one-dimensional random variable. Remarkable computational savings as well as good convergence properties and acceptance rates are the results.