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Efficient cosmological model selection with Bayesian Optimisation

Not scheduled
15m
Auditorium 1, Convention Center (IIT Hyderabad)

Auditorium 1, Convention Center

IIT Hyderabad

Plenary Plenary

Speaker

Jan Hamann (The University of New South Wales)

Description

How can we decide which cosmological model is the most probable? In a Bayesian approach to statistics, this question can be readily answered using the framework of Bayesian model selection: namely by calculating a model's evidence. However, the numerical evaluation of the evidence can be a numerically difficult task.
I will introduce a new, efficient machine-learning approach to this problem – based on Gaussian Process Regression and Bayesian Optimisation and designed to minimise the number of likelihood evaluations required – and demonstrate its efficiency on a number of examples.

Track type Cosmology

Author

Jan Hamann (The University of New South Wales)

Presentation materials