ArticleFrontiers in physiology2018
Partial Autoinformation to Characterize Symbolic Sequences.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- EEG microstate syntax analysis: A review of methodological challenges and advances.NeuroImage · 2025Review
- Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep.Brain topography · 2024Article
- Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
An information-theoretic approach to numerically determine the Markov order of discrete stochastic processes defined over a finite state space is introduced. To measure statistical dependencies between different time points of symbolic time series, two information-theoretic measures are proposed. The first measure is time-lagged mutual information between the random variables
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.