UNIVERSAL CODING AND PREDICTION ON ERGODIC RANDOM POINTS
DOI10.1017/bsl.2022.18zbMath1501.94020arXiv2005.03627OpenAlexW4225403177WikidataQ114119608 ScholiaQ114119608MaRDI QIDQ5044311
Łukasz Dębowski, Tomasz Steifer
Publication date: 25 October 2022
Published in: The Bulletin of Symbolic Logic (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.03627
universal codingalgorithmic randomnessstationary ergodic processesprediction by partial matchinguniversal prediction
Inference from stochastic processes and prediction (62M20) Nonparametric estimation (62G05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Source coding (94A29) Algorithmic randomness and dimension (03D32)
Cites Work
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- A constructive version of Birkhoff's ergodic theorem for Martin-Löf random points
- Effectively closed sets of measures and randomness
- Upcrossing inequalities for stationary sequences and applications
- On a definition of random sequences with respect to conditional probability
- A sandwich proof of the Shannon-McMillan-Breiman theorem
- Prediction of random sequences and universal coding
- Guessing the next output of a stationary process
- Ergodic theorems for individual random sequences
- Prequential probability: principles and properties
- Prediction and dimension
- On universal algorithms for classifying and predicting stationary processes
- Generalised entropies and asymptotic complexities of languages
- Computability of probability measures and Martin-Löf randomness over metric spaces
- Weighted sums of certain dependent random variables
- Ergodic theory, entropy
- Uniform test of algorithmic randomness over a general space
- Randomness and Non-ergodic Systems
- Compression-Based Methods of Statistical Analysis and Prediction of Time Series
- Martin-Löf random points satisfy Birkhoff’s ergodic theorem for effectively closed sets
- Randomness for non-computable measures
- The dimension of ergodic random sequences
- Algorithmic Randomness and Complexity
- The Individual Ergodic Theorem of Information Theory
- The Smallest Grammar Problem
- RECOGNIZING STRONG RANDOM REALS
- Applications of Effective Probability Theory to Martin-Löf Randomness
- Randomness conservation inequalities; information and independence in mathematical theories
- A universal algorithm for sequential data compression
- A simple randomized algorithm for sequential prediction of ergodic time series
- Grammar-based codes: a new class of universal lossless source codes
- The strong law of large numbers for sequential decisions under uncertainty
- Compression-Based Methods for Nonparametric Prediction and Estimation of Some Characteristics of Time Series
- Measures and their random reals
- On the Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts
- Two inequalities implied by unique decipherability
- The definition of random sequences
- A formal theory of inductive inference. Part II
- A Note on the Ergodic Theorem of Information Theory
- Information Theory Meets Power Laws
- Information Theory
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