Many Parameters, Many Problems

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• Jaime Ruiz Zapatero
• Tuesday 31 October 2023, 16:00-17:00
• Martin Ryle Seminar Room, Kavli Institute.

If you have a question about this talk, please contact Will Barker.

Statistical Inference is the process by which the properties of a population are inferred from a sample. In Cosmology this translates to learning the properties of the Universe from a handful of galaxies. The difficulty of this exercise greatly depends on the number of properties we want to learn, i.e. the number of parameters of our model. In this talk I will motivate why future cosmological models will inevitably possess hundreds if not thousands of parameters, describe how the traditional techniques we currently use to constrain our models grow inefficient for these number of dimensions and propose possible solutions. Namely, I will present our recent work (2301.11895 & 2301.11978) developing analytical marginalization schemes for “nuisance” parameters in the context 3×2 analyses. I will also discuss our current work developing gradient-based marginalization techniques to solve the problem numerically.

This talk is part of the Hills Coffee Talks series.

This talk is included in these lists:

• All Cavendish Laboratory Seminars
• All Talks (aka the CURE list)
• Cambridge Astronomy Talks
• Cavendish Astrophysics Seminars
• Centre for Health Leadership and Enterprise
• Combined External Astrophysics Talks DAMTP
• Cosmology, Astrophysics and General Relativity
• Featured lists
• Hills Coffee Talks
• ME Seminar
• Martin Ryle Seminar Room, Kavli Institute
• Neurons, Fake News, DNA and your iPhone: The Mathematics of Information
• School of Physical Sciences
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