Astrostatistical Challenges for the New Astronomy: 1 (Springer Series in Astrostatistics)

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  1. Astrostatistics
  2. Joseph Hilbe
  3. Dr. Joseph Hilbe
  4. Astrostatistical Challenges for the New Astronomy |

Bremer M. Valtchanov I. The near--infrared luminosity function and density. Paolillo M. Sabatier, Toulouse, research director: E. Davoust; rapporteurs: M.


Capaccioli, A. Mazure, E.

  • Joseph Hilbe.
  • Jaane Bhee Do Yaaro.
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The properties of galaxies in Coma. Enlarging the sample and gaining in precision.

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The non-uniform distribution of galaxies in the inner region of Perseus. Editorial Fontalba, Barcelona, , Astrophysical consequences of an observational bias. Servolo, August , to be published in Il Nuovo Cimento. Prospects for the XMM next decade. Contents and Structure of the Universe".

Joseph Hilbe

MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence or model likelihood, the prime quantity for Bayesian model comparison together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.

We demonstrate the power and flexibility of MultiNest for Bayesian inference for multi-dimensional, multimodal-likelihoods, for Bayesian model selection and for profile likelihood evaluation for multi-modal, multi-scale likelihoods. Applications in cosmology and astroparticle physics are presented, including gravitational waves astronomy, inflationary Bayesian model comparison and supersymmetric parameter spaces exploration. FMCMC is a new general purpose tool for nonlinear model fitting.

It incorporates parallel tempering, simulated annealing and genetic crossover operations. Each of these features facilitate the detection of a global minimum in chi-squared in a highly multi-modal environment. By combining all three, the algorithm greatly increases the probability of realizing this goal. The star-galaxy separator is a statistical classification method which outputs class membership probabilities rather than class labels and allows the use of prior knowledge about the source populations.

The anomaly detection method addresses the problem posed by objects having different sets of recorded variables in cross-matched datasets.

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  4. This prevents the use of methods unable to handle missing values and makes direct comparison between objects difficult. For each source, our method computes anomaly scores in subspaces of the observed feature space and combines them to an overall anomaly score.

    Dr. Joseph Hilbe

    The proposed technique is very general and can easily be used in applications other than astronomy. The properties and performance of our method are investigated using both real and simulated datasets. Classification of galaxies has been carried out by using two recently developed methods, viz. The first two sets are consisting of dwarf galaxies and their globular clusters whose distributions are non Gaussian in nature. The third one is a larger one containing a wider range of galaxies consisting of dwarfs to giants in 56 clusters of galaxies.

    Morphological classification of galaxies are subjective in nature and as a result can not properly explain the formation mechanism and other related issues under the influence of different correlated variables through a proper scientific approach. Hence objective classification by using the above mentioned methods are preferred to overcome the loopholes. Cardiff University Libraries. City, University of London.

    Astronomy/Astrophysics Part 2

    University of Dundee. Edinburgh Napier University. Glasgow Caledonian University.

    Astrostatistical Challenges for the New Astronomy |

    University of Glasgow Library. Imperial College London Library. University of Leeds Library. University of Liverpool Library. University of Manchester Library. Newcastle University Libraries. Queen Margaret University Library.