1,475 research outputs found

    Closure of Resource-Bounded Randomness Notions Under Polynomial-Time Permutations

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    An infinite bit sequence is called recursively random if no computable strategy betting along the sequence has unbounded capital. It is well-known that the property of recursive randomness is closed under computable permutations. We investigate analogous statements for randomness notions defined by betting strategies that are computable within resource bounds. Suppose that S is a polynomial time computable permutation of the set of strings over the unary alphabet (identified with the set of natural numbers). If the inverse of S is not polynomially bounded, it is not hard to build a polynomial time random bit sequence Z such that Z o S is not polynomial time random. So one should only consider permutations S satisfying the extra condition that the inverse is polynomially bounded. Now the closure depends on additional assumptions in complexity theory. Our first main result, Theorem 4, shows that if BPP contains a superpolynomial deterministic time class, then polynomial time randomness is not preserved by some permutation S such that in fact both S and its inverse are in P. Our second result, Theorem 11, shows that polynomial space randomness is preserved by polynomial time permutations with polynomially bounded inverse, so if P = PSPACE then polynomial time randomness is preserved

    Likelihood test in permutations with bias. Premier League and La Liga: surprises during the last 25 seasons

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    In this paper, we introduce the models of permutations with bias, which are random permutations of a set, biased by some preference values. We present a new parametric test, together with an efficient way to calculate its p-value. The final tables of the English and Spanish major soccer leagues are tested according to this new procedure, to discover whether these results were aligned with expectations.Comment: Bibliography updated. Thanks to Prof Karlsson to have suggested the paper [8] H. Stern. Models for distributions on permutations. JASA (1990

    Quantum probabilities as Bayesian probabilities

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    In the Bayesian approach to probability theory, probability quantifies a degree of belief for a single trial, without any a priori connection to limiting frequencies. In this paper we show that, despite being prescribed by a fundamental law, probabilities for individual quantum systems can be understood within the Bayesian approach. We argue that the distinction between classical and quantum probabilities lies not in their definition, but in the nature of the information they encode. In the classical world, maximal information about a physical system is complete in the sense of providing definite answers for all possible questions that can be asked of the system. In the quantum world, maximal information is not complete and cannot be completed. Using this distinction, we show that any Bayesian probability assignment in quantum mechanics must have the form of the quantum probability rule, that maximal information about a quantum system leads to a unique quantum-state assignment, and that quantum theory provides a stronger connection between probability and measured frequency than can be justified classically. Finally we give a Bayesian formulation of quantum-state tomography.Comment: 6 pages, Latex, final versio

    Common Knowledge and Disparate Priors: When it is O.K. to Agree to Disagree

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    Abandoning the oft-presumed common prior assumption, partitioned type spaces with disparate priors are studied. It is shown that in the two-player case, a unique fundamental pair of priors can be identified in each type space, from whose properties boundaries on the possible ranges of expected values under common knowledge can be derived. In the limit as the elements of this pair approach each other,a common prior is identified, and standard results stemming from the common prior assumption are recapitulated. It is further shown that this two-player fundamental pair of priors is a special case of the n-player situation, where a representative n-tuple of fundamentally associated priors can be selected, out of at most n-1 such n-tuples, to play an analogous role.common knowledge; heterogeneous prior beliefs; common prior assumption
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