Bayesian methods for cosmological parameter estimation from cosmic microwave background measurements

By N. Christensen, Renate Meyer, L. Knox, B. Luey. 2000

169 citations recorded by the source corpus.

We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave background. Our strategy relies on Markov chain Monte Carlo methods, specifically the Metropolis-Hastings algorithm, to perform the necessary high-dimensional integrals. We describe the Metropolis-Hastings algorithm in detail and discuss the results of our test on simulated data.

Subfields: Artificial Intelligence, Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/1f3d93e643a01d02fc21cd127b118195a245a238

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In Artificial Intelligence

In Astronomy and Astrophysics

Citations in the corpus

A MEASUREMENT OF GRAVITATIONAL LENSING OF THE MICROWAVE BACKGROUND USING SOUTH POLE TELESCOPE DATA (2012, methodology): “To explore high-dimensional parameter volumes, we use Monte Carlo Markov Chain (MCMC) techniques (Christensen et al. 2001; Lewis & Bridle 2002).”
Evidence of the accelerated expansion of the Universe from weak lensing tomography with COSMOS (2009, methodology): “…technique (Cappé et al. 2007): instead of creating a sample under the posterior as done in traditional Monte-Carlo Markov chain (MCMC) techniques (e.g. Christensen et al. 2001), points are sampled from a simple distribution, the so-called proposal, in our case a mixture of eight Gaussians.”
Detection of polarization in the cosmic microwave background using DASI (2002, methodology): “For each analysis we also implement a Markov chain evaluation of the likelihood functio”
Cosmological Parameters from the 2003 Flight of BOOMERANG (2005, methodology): “Parameter Estimation Methodology The Monte Carlo Markov Chain (MCMC) sampling technique we use for parameter estimation is described in detail in Neal (1993); Christensen et al. (2001); Lewis & Bridle (2002) and implemented in the publicly available CosmoMC21 package.”
CONSTRAINTS ON COSMOLOGY FROM THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM OF THE 2500 deg2 SPT-SZ SURVEY (2012, methodology): “Parameter Estimation Methodology Posterior probability distributions for the parameters are calculated using Markov Chain Monte Carlo (MCMC) methods (Christensen et al. 2001) with the publicly available CosmoMC27 package (Lewis & Bridle 2002).”
Statistical inference for dynamical systems: A review (2012, methodology): “v chain Monte Carlo (MCMC) methods. For a discussion of such methods, see the books [105, 124] and references therein. Such methods have been used for parameter estimation in dynamical systems (e.g., [21]). Traditional Monte Carlo or MCMC methods may be used to perform \batch" inference, i.e. w”
Reconstructing the primordial power spectrum (2003, background): “For further details see Lewis and Bridle (2002); Christensen et al. (2001); Verde et al. (2003) and references therein.”
A MEASUREMENT OF THE COSMIC MICROWAVE BACKGROUND DAMPING TAIL FROM THE 2500-SQUARE-DEGREE SPT-SZ SURVEY (2012, methodology): “We explore the nine-dimensional parameter space using a Markov Chain Monte Carlo (MCMC) technique (Christensen et al. 2001) implemented in the CosmoMC32 (Lewis & Bridle 2002) software package.”