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Hilbert space multidimensional modelling of continuous measurements
Author(s) -
Jerome R. Busemeyer,
Z. Wang
Publication year - 2019
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2019.0142
Subject(s) - kochen–specker theorem , hilbert space , context (archaeology) , joint probability distribution , rigged hilbert space , hidden variable theory , computer science , set (abstract data type) , marginal distribution , space (punctuation) , mathematics , probability distribution , vector space , statistical physics , reproducing kernel hilbert space , pure mathematics , quantum , random variable , statistics , quantum mechanics , physics , paleontology , biology , programming language , operating system
Data fusion problems arise when a researcher needs to analyse results obtained by measuring empirical variables under different measurement contexts. A context is defined by a subset of variables taken from a complete set of variables under investigation. Multiple contexts can be formed from different subsets, which produce a separate distribution of measurements associated with each context. A context effect occurs when the distributions produced by the different contexts cannot be reproduced by marginalizing over a complete joint distribution formed by all the variables. We propose a Hilbert space multidimensional theory that uses a state vector and measurement operators to account for multiple distributions produced by different contexts. This article is part of the theme issue ‘Contextuality and probability in quantum mechanics and beyond’.

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