A. García Nogales, P. Pérez Fernández

A classic Phillips result on the relationship between independence and conditional
independence appears here as a particular case of a result recently published by
the authors. Specifically, this result establishes that, given random variables X, Y,Z and
under the conditional independence of X and Y given Z, the independence of X and Y
is equivalent to the independence (as Markov kernels) of the conditional distributions of
X given Z and of Y given Z.

Keywords: Conditional independence, Markov kernel


Posters I
June 7, 2022  12:00 PM
Faculty Hall

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Estimación distribuida en sistemas con múltiples sensores sujetos a fallos aleatorios

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Estimación en sistemas en red con topología conocida y medidas deterioradas

R. Caballero-Águila, M. A. Carazo, J. Linares Pérez

Estimación óptima de señales tessarine mediante observaciones inciertas multisensoriales

R. M. Fernández Alcalá, J. D. Jiménez López, J. Navarro Moreno, J. C. Ruiz Molina

Some notes about statistical inference for a new Gompertz diffusion process with exogenous factors

N. Makhlouk, A. Nafidi, R. Gutiérrez Sánchez, E. Ramos-Abalos, R. Gutiérrez Sánchez

Un tratamiento basado en clústeres para la estimación fusión distribuida

R. Caballero-Águila, M. A. Hermoso Carazo, J. Linares Pérez

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