DISCRETIZING DISTRIBUTIONS WITH EXACT MOMENTS: ERROR ESTIMATE AND CONVERGENCE ANALYSIS

作者：Tanaka Ken'ichiro^*; Toda Alexis Akira

来源：SIAM Journal on Numerical Analysis, 2015, 53(5): 2158-2177.

DOI：10.1137/140971269

• probability distribution
• discrete approximation
• generalized moment
• quadrature formula
• Kullback-Leibler information
• Fenchel duality

The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This paper considers the problem of discretizing a continuous distribution, which arises in various applied fields. We obtain the approximating distribution by minimizing the Kullback-Leibler information (relative entropy) of the unknown discrete distribution relative to an initial discretization based on a quadrature formula subject to some moment constraints. We study the theoretical error bound and the convergence of this approximation method as the number of discrete points increases. We prove that (i) the theoretical error bound of the approximate expectation of any bounded continuous function has at most the same order as the quadrature formula we start with, and (ii) the approximate discrete distribution weakly converges to the given continuous distribution. Moreover, we present some numerical examples that show the advantage of the method and apply to numerically solving an optimal portfolio problem.

• 出版日期2015