Probabilistic models on contextuality scenarios
نویسندگان
چکیده
منابع مشابه
A Qualified Kolmogorovian Account of Probabilistic Contextuality
We describe a mathematical language for determining all possible patterns of contextuality in the dependence of stochastic outputs of a system on its deterministic inputs. The central principle (contextualityby-default) is that the outputs indexed by mutually incompatible values of inputs are stochastically unrelated; but they can be coupled (imposed a joint distribution on) in a variety of way...
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We propose a principled way of defining and measuring contextuality in systems with deterministic inputs and random outputs. We illustrate it on systems with two binary inputs and two binary random outputs, the prominent example being the system of two entangled spin-half particles with each particle’s spins (random outputs) being measured along one of two directions (inputs). It is traditional...
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Models of a phenomenon are often developed by examining it under different experimental conditions, or measurement contexts. The resultant probabilistic models assume that the underlying random variables, which define a measurable set of outcomes, can be defined independent of the measurement context. The phenomenon is deemed contextual when this assumption fails. Contextuality is an important ...
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Counterfactual reasoning and contextuality is defined and critically evaluated with regard to its nonempirical content. To this end, a uniqueness property of states, explosion views and link observables are introduced. If only a single context associated with a particular maximum set of observables can be operationalized, then a context translation principle resolves measurements of different c...
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Traditional scenario of probabilistic modelling is directed at generating samples having a given probability distribution. We argue that this scenario is impracticable for image modelling and should be replaced by more reasonable alternative one. This latter tends to generate samples having probabilities in the vicinity of the probability of a given training sample. Gibbs image models with mult...
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ژورنال
عنوان ژورنال: Electronic Proceedings in Theoretical Computer Science
سال: 2014
ISSN: 2075-2180
DOI: 10.4204/eptcs.171.6