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2011

Understanding better (some) astronomical data using Bayesian methods

Andreon, S.

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Understand

Current analysis of astronomical data are confronted with the daunting task of modeling the awkward features of astronomical data, among which heteroscedastic (point-dependent) errors, intrinsic scatter, non-ignorable data collection (selection effects), data structure, non-uniform populations (often called Malmquist bias), non-Gaussian data, and upper/lower limits.

This chapter shows, by examples, how modeling all these features using Bayesian methods.
In short, one just need to formalize, using maths, the logical link between the involved quantities, how the data arise and what we already known on the quantities we want to study.
The posterior probability distribution summarizes what we known on the studied quantities after the data, and we should not be afraid about their actual numerical computation, because it is left to (special) Monte Carlo programs such as JAGS.

Understanding better (some) astronomical data using Bayesian methods · Around

Built on

Final Results from the Hubble Space Telescope Key Project to Measure the Hubble Constant
W. L. Freedman, B. F. Madore, B. K. Gibson, L. Ferrarese, D. D. Kelson, S. Sakai, J. R. Mould, R. C. Kennicutt, Jr., H. C. Ford, J. A. Graham, J. P. Huchra, S. M. G. Hughes, G. D. Illingworth, L. M. Macri, and P. B. Stetson · 2001
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Bayesian data analysis
A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin · 2004
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The BUGS project: Evolution, critique and future directions
D. Lunn, D. Spiegelhalter, A. Thomas, and N. Best · 2009
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