• Markov Chain Monte Carlo (MCMC);
  • intrinsic nonlinearity;
  • model error;
  • multiple minima;
  • multiple models;
  • predictive uncertainty

Heavy computation power, lengthy simulations, and an exhaustive number of model runs—often these seem like the only statistical tools that scientists have at their disposal when computing uncertainties associated with predictions, particularly in cases of environmental processes such as groundwater movement. However, calculation of uncertainties need not be as lengthy, a new study shows. Comparing two approaches—the classical Bayesian “credible interval” and a less commonly used regression-based “confidence interval” method—Lu et al. show that for many practical purposes both methods provide similar estimates of uncertainties. The advantage of the regression method is that it demands 10–1000 model runs, whereas the classical Bayesian approach requires 10,000 to millions of model runs.