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On dice and coins: models of computation for random generation

### David Feldman
Russell Impagliazzo Moni Naor
Noam Nisan Steven Rudich Adi Shamir

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In this work we
examine the concept of random generation in bounded, as opposed to * expected*, polynomial time. In order to achieve random generation
with such a property, a model of a Probabilistic Turing Machine with the ability to make random choices with any (small) rational bias is necessary.
This ability is equivalent to that of being able to simulate rolling any k-sided die (where k is polynomial in the length of the input).
We would like to minimize the amount of hardware required for a machine with this capability.
This leads to the problem of efficiently simulating a family of dice with a few different types of biased coins as possible.
In the special case of simulating one n-sided die, we prove that only two types of biased coins are necessary,
which can be reduced to one if we allow irrationally biased coins.
This simulation is efficient, taking O(log n) coin flips.

For the general case, where we want to simulate for all k < n a k-sided die,
we get a tight (time vs. number of biases) tradeoff; for example, with O(log n) different biases,
we can simulate, for any k < n, an k-sided die in O(log n) coin flips.

Paper:
PDF.

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