Mapping electron density in the ionosphere: A principal component MCMC algorithm
AbstractThe outer layers of the Earth's atmosphere are known as the ionosphere, a plasma of free electrons and positively charged atomic ions. The electron density of the ionosphere varies considerably with time of day, season, geographical location and the sun's activity. Maps of electron density are required because local changes in this density can produce inaccuracies in the Navy Navigation Satellite System (NNSS) and Global Positioning System (GPS). Satellite to ground based receiver measurements produce tomographic information about the density in the form of path integrated snapshots of the total electron content which must be inverted to generate electron density maps. A Bayesian approach is proposed for solving the inversion problem using spatial priors in a parsimonious model for the variation of electron density with height. The Bayesian approach to modelling and inference provides estimates of electron density along with a measure of uncertainty for these estimates, leading to credible intervals for all quantities of interest. The standard parameterisation does not lend itself well to standard Metropolis-Hastings algorithms. A much more efficient form of Markov chain Monte Carlo sampler is developed using a transformation of variables based on a principal components analysis of initial output.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/csda
Bayesian modelling Ionospheric mapping Inversion Markov chain Monte Carlo Principal components Tomography;
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- H�vard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392.
- Leonhard Knorr-Held, 2002. "On Block Updating in Markov Random Field Models for Disease Mapping," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 29(4), pages 597-614.
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