A Bayesian approach to inverse modelling of stratigraphy, part 1: method

We propose a new global optimization method ({\em Simulated Tempering}) for simulating effectively a system with a rough free energy landscape (i.e. many coexisting states) at finite non-zero temperature. This method is related to simulated annealing, but here the temperature becomes a dynamic variable, and the system is always kept at equilibrium. We analyze the method on the Random Field Ising Model, and we find a dramatic improvement over conventional Metropolis and cluster methods. We analyze and discuss the conditions under which the method has optimal performances.