ArticleEntropy (Basel, Switzerland)2026
Knowing What We Don't Know: Model-Based Uncertainty Decomposition for Categorical Sequences.
Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Authors and funding
3 authors.
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Abstract
State sequence analysis of longitudinal categorical data seeks to synthesize pathways through different dimensions of the life course for descriptive, associative and predictive purposes. Given the number and variety of patterns in such data, measures of the dynamic features of sequences are used to characterize them. One, based on the information-theoretic notion of entropy, measures the uncertainty in the state that will be active at a given time. We customize its use to establish the extent to which we are ignorant, or unsure, of
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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.