Contextuality Scenarios Arising from Networks of Stochastic Processes
An empirical model is a generalization of a probability space. It consists of a simplicial complex of subsets of a class of random variables such that each simplex has an associated probability distribution. The ensuing marginalizations are coherent, in the sense that the distribution on a face of a...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/60694 |
| Acceso en línea: | http://hdl.handle.net/11336/60694 |
| Access Level: | acceso abierto |
| Palabra clave: | Contextuality Empirical Models Open Stochastic Processes https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | An empirical model is a generalization of a probability space. It consists of a simplicial complex of subsets of a class of random variables such that each simplex has an associated probability distribution. The ensuing marginalizations are coherent, in the sense that the distribution on a face of a simplex coincides with the marginal of the distribution over the entire simplex. An empirical model is called contextual if its distributions cannot be obtained by marginalizing a joint distribution over . Contextual empirical models arise naturally in quantum theory, giving rise to some of its counter -intuitive statistical consequences. In this paper, we present a different and classical source of contextual empirical models: the interaction among many stochastic processes. We attach an empirical model to the ensuing network in which each node represents an open stochastic process with input and output random variables. The statistical behaviour of the network in the long run makes the empirical model generically contextual and even strongly contextual. |
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