Bayesian deep learning for dark energy

Abstract

In this work we discuss basic ideas on how to structure and study the Bayesian methods for standard models of dark energy and how to implement them in the architecture of deep learning processes.

Invited chapter for "Cosmology 2020 - The Current State" book

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Cited by in corpus (5)

• Teleparallel Gravity: From Theory to Cosmology
• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• f(T) cosmology against the cosmographic method: A new study using mock and observational data
• Model-Independent Perspectives on Coupled Dark Energy and the Swampland
• Hubble constant by natural selection: Evolution chips in the Hubble tension