Large Scale Bayesian Inference in Cosmology
APA
Jasche, J. (2013). Large Scale Bayesian Inference in Cosmology. Perimeter Institute. https://pirsa.org/13040119
MLA
Jasche, Jens. Large Scale Bayesian Inference in Cosmology. Perimeter Institute, Apr. 11, 2013, https://pirsa.org/13040119
BibTex
@misc{ pirsa_PIRSA:13040119,
doi = {10.48660/13040119},
url = {https://pirsa.org/13040119},
author = {Jasche, Jens},
keywords = {Cosmology},
language = {en},
title = {Large Scale Bayesian Inference in Cosmology},
publisher = {Perimeter Institute},
year = {2013},
month = {apr},
note = {PIRSA:13040119 see, \url{https://pirsa.org}}
}
Institut d'Astrophysique de Paris
Collection
Talk Type
Subject
Abstract
Already the last decade has
witnessed unprecedented progress in the collection of cosmological data.
Presently proposed and designed future cosmological probes and surveys permit
us to anticipate the upcoming avalanche of cosmological information during the
next decades.
The increase of valuable observations needs to be accompanied with the development of efficient and accurate information processing technology in order to analyse and interpret this data. In particular, cosmography projects, aiming at studying the origin and inhomogeneous evolution of
the Universe, involve high dimensional inference methods. For example, 3d cosmological density and velocity field inference requires to explore on the order of 10^7 or more parameters. Consequently, such projects critically rely on state-of-the-art information processing techniques
and, nevertheless, are often on the verge of numerical feasibility with present day computational resources. For this reason, in this talk I will address the problem of high dimensional Bayesian inference from cosmological data sets, subject to a variety of statistical and systematic uncertainties. In particular, I will focus on the discussion of selected Markov Chain Monte Carlo techniques, permitting to efficiently solve inference problems with on the order of 10^7 parameters. Furthermore, these methods will be exemplified in various cosmological applications, raging from 3d non-linear density and photometric redshift inference to 4d physical state inference. These techniques permit us to exploit cosmologically relevant information from
observations to unprecedented detail and hence will significantly contribute to the era of precision cosmology.
The increase of valuable observations needs to be accompanied with the development of efficient and accurate information processing technology in order to analyse and interpret this data. In particular, cosmography projects, aiming at studying the origin and inhomogeneous evolution of
the Universe, involve high dimensional inference methods. For example, 3d cosmological density and velocity field inference requires to explore on the order of 10^7 or more parameters. Consequently, such projects critically rely on state-of-the-art information processing techniques
and, nevertheless, are often on the verge of numerical feasibility with present day computational resources. For this reason, in this talk I will address the problem of high dimensional Bayesian inference from cosmological data sets, subject to a variety of statistical and systematic uncertainties. In particular, I will focus on the discussion of selected Markov Chain Monte Carlo techniques, permitting to efficiently solve inference problems with on the order of 10^7 parameters. Furthermore, these methods will be exemplified in various cosmological applications, raging from 3d non-linear density and photometric redshift inference to 4d physical state inference. These techniques permit us to exploit cosmologically relevant information from
observations to unprecedented detail and hence will significantly contribute to the era of precision cosmology.