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2 results

  • 6 - Conditioning, dependence, and joint moments

  • from Part A - Probability and Distribution Theory
  • Karim M. Abadir, Imperial College London, Risto D. H. Heijmans, Jan R. Magnus, Vrije Universiteit, Amsterdam
  • Book:
    Statistics
    Published online:
    11 June 2020
    Print publication:
    08 November 2018, pp 171-226

  • Summary

    This chapter concerns the measurement of the dependence between variates, by exploiting the additional information contained in joint (rather than just marginal) distribution and density functions. For this multivariate context, we also generalize the third description of randomness seen earlier, i.e., moments and their generating functions. Joint moments and their generating functions are introduced, along with covariances, variance matrices, the Cauchy–Schwarz inequality, and joint c.f.s and their inversion into joint densities. We show how the law of iterated expectations makes use of conditioning when taking expectations with respect to more than one variate. We measure dependence via conditional densities, distributions, moments, and cumulants.

  • A first glance into the Spectral Energy Distributions of Single Stellar Populations in the Infrared range

  • Sofia Meneses-Goytia, Reynier F. Peletier
  • Journal:
    Proceedings of the International Astronomical Union / Volume 7 / Issue S284 / September 2011
    Published online by Cambridge University Press:
    17 August 2012, pp. 32-34
    Print publication:
    September 2011
    • Article
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  • The present work shows the Spectral Energy Distributions (SEDs) in the infrared using the IRTF stellar library, obtained using models based on Single Stellar population Models (SSP). We have focused on the K band in order to compare with observables of elliptical galaxies. We also present the comparisons of our models with velocity dispersions, ages and metallicities obtained with models in the optical range.