Estimation of cosmological parameters using adaptive importance sampling

Physical review D: Particles and fields 03/2009; DOI:10.1103/PhysRevD.80.023507
Source: arXiv

ABSTRACT We present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov Chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower computational time for PMC. In the case of WMAP5 data, for example, the wall-clock time reduces from several days for MCMC to a few hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analysed and discussed. Comment: 17 pages, 11 figures

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    ABSTRACT: We perform a model-independent fit of the short-distance couplings C 7,9,10 within the Standard Model set of b → sγ and b → s $ \overline \ell $ ℓ operators. Our analysis of B → K ∗γ, B → K (∗) $ \overline \ell $ ℓ and B s → μμ decays is the first to harness the full power of the Bayesian approach: all major sources of theory uncertainty explicitly enter as nuisance parameters. Exploiting the latest measurements, the fit reveals a flipped-sign solution in addition to a Standard-Model-like solution for the couplings C i. Each solution contains about half of the posterior probability, and both have nearly equal goodness of fit. The Standard Model prediction is close to the best-fit point. No New Physics contributions are necessary to describe the current data. Benefitting from the improved posterior knowledge of the nuisance parameters, we predict ranges for currently unmeasured, optimized observables in the angular distributions of B → K ∗(→ Kπ) $ \overline \ell $ ℓ.
    Journal of High Energy Physics 2012(8). · 5.62 Impact Factor

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