Aug 14, 2012 · Abstract:Mutual information I in infinite sequences (and in their finite prefixes) is essential in theoretical analysis of many situations.
Kolmogorov-Martin-Lof Randomness concept is extended from computable to enumerable distributions, which allows definitions of various other properties, ...
Abstract. Mutual information I in infinite sequences (and in their finite prefixes) is essential in theoretical analysis of many situations.
Aug 2, 2021 · Abstract. Mutual information I in infinite sequences (and in their finite prefixes) is essential in theoretical analysis of many situations.
Kolmogorov-Martin-Lof Randomness concept is extended from computable to enumerable distributions. This allows definitions of various other properties, ...
Bibliographic details on Enumerable Distributions, Randomness, Dependence.
Abstract. We initiate a comprehensive study of the question of randomness extractions from two somewhat dependent sources of defective randomness.
Missing: Enumerable | Show results with:Enumerable
This reminds one of the theorem of classical probabilistic information theory in which two random variables are independent iff they have no mutual information.
Sep 25, 2024 · What kind of randomness do you want? What distribution, which speed vs quality tradeoff? Should it be reproducible or would that be a bad thing?
We show that local dependence endows random graph models with desirable properties which make them amenable to statistical inference.
Missing: Enumerable Randomness,