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Circular Analysis in Complex Stochastic Systems

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Journal Sci Rep
Specialty Science
Date 2015 Dec 15
PMID 26656656
Citations 2
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Abstract

Ruling out observations can lead to wrong models. This danger occurs unwillingly when one selects observations, experiments, simulations or time-series based on their outcome. In stochastic processes, conditioning on the future outcome biases all local transition probabilities and makes them consistent with the selected outcome. This circular self-consistency leads to models that are inconsistent with physical reality. It is also the reason why models built solely on macroscopic observations are prone to this fallacy.

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