Abstract
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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Mark, Z., Baram, Y. (2001). The Bias-Variance Dilemma of the Monte Carlo Method. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_20
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DOI: https://doi.org/10.1007/3-540-44668-0_20
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