3 research outputs found
Binarization Trees and Random Number Generation
An m-extracting procedure produces unbiased random bits from a loaded dice
with m faces. A binarization takes inputs from an m-faced dice and produce bit
sequences to be fed into a (binary) extracting procedure to obtain random bits.
Thus, binary extracting procedures give rise to an m-extracting procedure via a
binarization. An entropy- preserving binarization is to be called complete, and
such a procedure has been proposed by Zhou and Bruck. We show that there exist
complete binarizations in abundance as naturally arising from binary trees with
m leaves. The well-known leaf entropy theorem and a closely related structure
lemma play important roles in the arguments.Comment: 8 page
Peres-Style Recursive Algorithms
Peres algorithm applies the famous von Neumann trick recursively to produce
unbiased random bits from biased coin tosses. Its recursive nature makes the
algorithm simple and elegant, and yet its output rate approaches the
information-theoretic upper bound. However, it is relatively hard to explain
why it works, and it appears partly due to this difficulty that its
generalization to many-valued source was discovered only recently. Binarization
tree provides a new conceptual tool to understand the innerworkings of the
original Peres algorithm and the recently-found generalizations in both aspects
of the uniform random number generation and asymptotic optimality. Furthermore,
it facilitates finding many new Peres-style recursive algorithms that have been
arguably very hard to come by without this new tool.Comment: 15 pages. Version 2, as of May 22, 2018, includes a proof of
Structure Lemma and a few correction
Randomness extraction in computability theory
In this article, we study a notion of the extraction rate of Turing
functionals that translate between notions of randomness with respect to
different underlying probability measures. We analyze several classes of
extraction procedures: a first class that generalizes von Neumann's trick for
extracting unbiased randomness from the tosses of a biased coin, a second class
based on work of generating biased randomness from unbiased randomness by Knuth
and Yao, and a third class independently developed by Levin and Kautz that
generalizes the data compression technique of arithmetic coding. For the first
two classes of extraction procedures, we identify a level of algorithmic
randomness for an input that guarantees that we attain the extraction rate
along that input, while for the third class, we calculate the rate attained
along sufficiently random input sequences