COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Cosmology Lunch > Maximum Entropy Reasoning: From Cosmological Likelihood Functions to Quantum Field Theory

## Maximum Entropy Reasoning: From Cosmological Likelihood Functions to Quantum Field TheoryAdd to your list(s) Download to your calendar using vCal - Steven Gratton (IoA)
- Monday 22 November 2010, 13:00-14:00
- CMS, Pav.B, CTC Common Room (B1.19).
If you have a question about this talk, please contact Andrew Pontzen. In this talk I review the maximum entropy principle and discuss its use in obtaining usable probability distributions for measurable quantities. I argue that maximum entropy reasoning gives a Bayesian motivation for certain “high-l” cosmological likelihood approximations, useful for the analysis of large cosmic microwave background datasets such as that expected from the Planck mission. I then show how the principle provides a means to systematically improve such approximations should the need arise. In light of the reasoning applied, I end by discussing connections between maximum entropy reasoning and quantum field theory. This talk is part of the Cosmology Lunch series. ## This talk is included in these lists:- All CMS events
- CMS Events
- CMS, Pav.B, CTC Common Room (B1.19)
- Cosmology Lunch
- Cosmology lists
- Cosmology, Astrophysics and General Relativity
- DAMTP info aggregator
- Kavli Institute for Cosmology Talk Lists
- Priscilla
- bld31
Note that ex-directory lists are not shown. |
## Other listsChurchill Scholars Overly Awesome Research Symposium (ChuSOARS) The Emmy Noether Society: Women that Count Electron Microscopy Group Seminars## Other talksA unifying theory of branching morphogenesis Plastics in the Ocean: Challenges and Solutions Martin Roth: »Widerrede!« Brest-Litovsk and the Making of Modern Ukraine and Russia Constraint Analysis and Optimization in Medicine Development and Supply A compositional approach to scalable statistical modelling and computation |