A NOTE ON THE LEARNING-THEORETIC CHARACTERIZATIONS OF RANDOMNESS AND CONVERGENCE
From MaRDI portal
Publication:5099786
DOI10.1017/S1755020321000125OpenAlexW3138044606MaRDI QIDQ5099786
Publication date: 26 August 2022
Published in: The Review of Symbolic Logic (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/s1755020321000125
Cites Work
- Unnamed Item
- A constructive version of Birkhoff's ergodic theorem for Martin-Löf random points
- On a definition of random sequences with respect to conditional probability
- Guessing the next output of a stationary process
- Ergodic theorems for individual random sequences
- Predictions and algorithmic statistics for infinite sequences
- On Martin-Löf (non-)convergence of Solomonoff's universal mixture
- Algorithmic tests and randomness with respect to a class of measures
- Lectures on summability
- Martin-Löf random points satisfy Birkhoff’s ergodic theorem for effectively closed sets
- Schnorr randomness and the Lebesgue differentiation theorem
- Generic computability, Turing degrees, and asymptotic density
- Algorithmic Randomness and Complexity
- The Individual Ergodic Theorem of Information Theory
- RECOGNIZING STRONG RANDOM REALS
- Von Mises' definition of random sequences reconsidered
- The strong law of large numbers for sequential decisions under uncertainty
- Algorithmic Aspects of Lipschitz Functions
- Trial and error predicates and the solution to a problem of Mostowski
- Limiting recursion
- The definition of random sequences
- A formal theory of inductive inference. Part I
- A formal theory of inductive inference. Part II
This page was built for publication: A NOTE ON THE LEARNING-THEORETIC CHARACTERIZATIONS OF RANDOMNESS AND CONVERGENCE
