Circular analysis in complex stochastic systems.

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.

SUBMITTER: Valleriani A 

PROVIDER: S-EPMC4675072 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature