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Sharp entropy bounds for discrete statistical simulation

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  • Romik, Dan

Abstract

We define a general procedure for simulating a given discrete distribution using a sequence of i.i.d. random variables. This procedure is used to prove that a natural information-theoretic bound on the number of samples required to simulate the distribution can be arbitrarily approached in a limiting sense.

Suggested Citation

  • Romik, Dan, 1999. "Sharp entropy bounds for discrete statistical simulation," Statistics & Probability Letters, Elsevier, vol. 42(3), pages 219-227, April.
    • Handle: RePEc:eee:stapro:v:42:y:1999:i:3:p:219-227
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    Cited by:

    1. Nekrutkin Vladimir, 2016. "On the complexity of binary floating point pseudorandom generation," Monte Carlo Methods and Applications, De Gruyter, vol. 22(2), pages 109-116, June.

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