Sutter, TobiasSutter, DavidEsfahani, Peyman MohajerinLygeros, John2019-10-312019-10-312019-10-312019-01-01https://infoscience.epfl.ch/handle/20.500.14299/162524WOS:000491132200002We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.Automation & Control SystemsComputer Science, Artificial IntelligenceComputer Scienceentropy maximizationconvex optimizationrelative entropy minimizationfast gradient methodapproximate dynamic programmingmoment-closureminimizationefficienttext::journal::journal article::research article