Informed proposal Monte Carlo

Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on fly, much improvement has been made in this way. However, as a consequence No-Free-Lunch Theorem, only way we can ensure significantly better performance is build possible. In special case Markov Chain Monte Carlo (MCMC) review how done through choice proposal distribution, show adding more particularly efficient when based an approximate physics model problem. A highly nonlinear scattering with high-dimensional space serves illustration gain efficiency approach.