Random networks tossing biased coins

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Random networks tossing biased coins.

In statistical mechanical investigations of complex networks, it is useful to employ random graph ensembles as null models to compare with experimental realizations. Motivated by transcription networks, we present here a simple way to generate an ensemble of random directed graphs with asymptotically, scale-free out-degree and compact in-degree. Entries in each row of the adjacency matrix are s...

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Tossing a Biased Coin

When we talk about a coin toss, we think of it as unbiased: with probability one-half it comes up heads, and with probability one-half it comes up tails. An ideal unbiased coin might not correctly model a real coin, which could be biased slightly one way or another. After all, real life is rarely fair. This possibility leads us to an interesting mathematical and computational question. Is there...

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Identifying several biased coins encountered by a hidden random walk

Suppose that attached to each site z ∈ Z is a coin with bias θ(z), and only finitely many of these coins have non-zero bias. Allow a simple random walker to generate observations by tossing, at each move, the coin attached to its current position. Then we can determine the biases {θ(z)}z∈Z, using only the outcomes of these coin tosses and no information about the path of the random walker, up t...

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Using Biased Coins as Oracles

While it is well known that a Turing machine equipped with the ability to flip a fair coin cannot compute more than a standard Turing machine, we show that this is not true for a biased coin. Indeed, any oracle set X may be coded as a probability pX such that if a Turing machine is given a coin which lands heads with probability pX it can compute any function recursive in X with arbitrarily hig...

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ژورنال

عنوان ژورنال: Physical Review E

سال: 2007

ISSN: 1539-3755,1550-2376

DOI: 10.1103/physreve.75.056109