Results 21 to 30 of about 1,307,610 (290)
Entropy, 2018 We introduce a definition of algorithmic symmetry in the context of geometric and spatial complexity able to capture mathematical aspects of different objects using as a case study polyominoes and polyhedral graphs.
Hector Zenil +2 more
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Algorithmic Identification of Probabilities
CoRR, 2013 TThe problem is to identify a probability associated with a set of natural numbers, given an infinite data sequence of elements from the set. If the given sequence is drawn i.i.d. and the probability mass function involved (the target) belongs to a computably enumerable (c.e.) or co-computably enumerable (co-c.e.) set of computable probability mass ...
Paul M. B. Vitányi, Nick Chater
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Entropy, 2018 We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based on Solomonoff–Levin’s theory of algorithmic ...
Hector Zenil +5 more
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Algorithmic identification of probabilities is hard [PDF]
Journal of Computer and System Sciences, 2018 Suppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter $p$. By the law of large numbers, the frequency of zeros in the sequence tends to~$p$, and thus we can get better and better approximations of $p$ as we read the sequence.
Bienvenu, Laurent +3 more
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A Review of Graph and Network Complexity from an Algorithmic Information Perspective
Entropy, 2018 Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths
Hector Zenil +2 more
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On the Algorithmic Information Between Probabilities
CoRR, 2023 We extend algorithmic conservation inequalities to probability measures. The amount of self information of a probability measure cannot increase when submitted to randomized processing. This includes (potentially non-computable) measures over finite sequences, infinite sequences, and $T_0$, second countable topologies. One example is the convolution of
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Algorithms that Satisfy a Stopping Criterion, Probably [PDF]
Vietnam Journal of Mathematics, 2015 Iterative numerical algorithms are typically equipped with a stopping criterion, where the iteration process is terminated when some error or misfit measure is deemed to be below a given tolerance. This is a useful setting for comparing algorithm performance, among other purposes.
Ascher, Uri, Roosta-Khorasani, Farbod
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Algorithmic Identification of Probabilities Is Hard [PDF]
, 2014 Suppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter p. By the law of large numbers, the frequency of zeros in the sequence tends to p, and thus we can get better and better approximations of p as we read the sequence.
Bienvenu, Laurent +2 more
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Entropy, 2019 The principle of maximum entropy (Maxent) is often used to obtain prior probability distributions as a method to obtain a Gibbs measure under some restriction giving the probability that a system will be in a certain state compared to the rest of the ...
Hector Zenil +2 more
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Genealogy of Algorithms: Datafication as Transvaluation
Genealogy+Critique, 2020 This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm.
Virgil W. Brower
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