On the Distribution of the Information Density of Gaussian Random Vectors: Explicit Formulas and Tight Approximations

Based on the canonical correlation analysis, we derive series representations of the probability density function (PDF) and the cumulative distribution function (CDF) of the information density of arbitrary Gaussian random vectors as well as a general formula to calculate the central moments. Using...

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Detalles Bibliográficos
Autores principales: Huffmann, Jonathan E. W., Mittelbach, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323744/
https://www.ncbi.nlm.nih.gov/pubmed/35885147
http://dx.doi.org/10.3390/e24070924

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323744/
https://www.ncbi.nlm.nih.gov/pubmed/35885147
http://dx.doi.org/10.3390/e24070924

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