Seminars and Colloquia at ESO/Santiago

For ESO and ESO-related Conferences and Workshops in Europe and Chile please check the main Conferences and Workshops page.


Broadcast of the ESO talks is available upon request.  If anyone is interested, kindly contact us via email
at least 30 min prior  to the beginning of the talk.


February 2016

25.02.16 (Thursday)
12:00
"A pragmatic Bayesian perspective on correlation analysis: The exoplanetary gravity - stellar activity case"
Pedro FIGUEIRA (University of Porto)
Abstract
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"A pragmatic Bayesian perspective on correlation analysis: The exoplanetary gravity - stellar activity case"

Pedro FIGUEIRA (University of Porto)

Abstract

Very often we want to assess the if there is a correlation between two quantities. This is often done in the frequentist framework, using p-value analysis; unfortunately, it has been demonstrated that there are some fundamental flaws behind this kind of null hypothesis testing procedure. We consider the alternative approach of applying the Bayesian framework. To do so, we estimate the probability distribution of the parameter of interest, $\rho$, characterizing the strength of the correlation. We provide an implementation of these ideas and concepts using python programming language and the pyMC module in a very short (~130 lines of code, heavily commented) and user-friendly program. We used this tool to assess the presence and properties of the correlation between planetary surface gravity and stellar activity level as measured by the log(R'_HK) indicator. The results of the Bayesian analysis are qualitatively similar to those obtained via p-value analysis, and support the presence of a correlation in the data. The results are more robust in their derivation and more informative, revealing interesting features such as asymmetric posterior distributions or markedly different credible intervals, and allowing for a deeper exploration. We encourage those interested in this kind of problem to apply our code to his/her own scientific problems. The full understanding of what the Bayesian framework is can only be gained through the insight that comes by handling priors, assessing the convergence of Monte Carlo runs, and a multitude of other practical problems. We hope to contribute so that Bayesian analysis becomes a tool in the toolkit of researchers, and they understand by experience its advantages and limitations.


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