We investigate the setting in which Monte Carlo methods are used and draw a parallel to the formal setting of statistical inference. In particular, we find that Monte Carlo approximation gives rise to a bias-variance dilemma. We show that it is possible to construct a biased approximation scheme with a lower approximation error than a related unbiased algorithm.
CITATION STYLE
Mark, Z., & Baram, Y. (2001). The bias-variance dilemma of the Monte Carlo method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 141–147). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_20
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