Abstract
In this chapter we consider examples for which we want to estimate more than a single parameter. These common problems include estimating location and spread. We also consider examples that require the modeling of two populations (one we are interested in and a nuisance population) or averaging incompatible measurements. We also introduce quite complex examples dealing with upper limits and with a larger–than–expected scatter.
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- 1.
If this example sounds familiar to you, then you are an experienced astronomer. If this sounds unfamiliar, for decades astronomers discussed how to reconcile incompatible values of the Hubble constant.
- 2.
This section can be skipped the first time through.
- 3.
This is one of the many data features useful for discriminating interesting events from background events.
- 4.
It would perhaps be more correct to say that the first author of this book adopted a Bayesian approach after Giulio D’Agostini wrote, on his request, this paper.
- 5.
The data can be found at http://www.brera.mi.astro.it/ andreon/BayesianMethodsForThePhysi calSciences/comaS0FL.dat.R.
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Andreon, S., Weaver, B. (2015). Multi-parameters Models. In: Bayesian Methods for the Physical Sciences. Springer Series in Astrostatistics, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-15287-5_6
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