Abstract
Many of the concepts of building, running, and summarizing the results of a Bayesian analysis are described with this step-by-step guide using a basic (Gaussian) model. The chapter also introduces examples using Poisson and Binomial likelihoods, and how to combine repeated independent measurements.
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Notes
- 1.
The experiment is real, but dated, for reasons that will be self-evident by the end of this section.
- 2.
The author’s starting galaxy sample is larger, but not all galaxies from their sample can be recognized as having a nearby companion, even if they truly have one. This is due to observational limitations. Therefore, using the same approach as the authors, we adopted an effective sample of 2127 galaxies.
References
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Spiegelhalter, D., Thomas A., Best N., Gilks W., BUGS: Bayesian inference Using Gibbs Sampling, Version 0.5, 1996 http://www.mrc-bsu.cam.ac.uk/bugs/documentation/Download/manual05.pdf
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Andreon, S., Weaver, B. (2015). Single Parameter 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_4
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DOI: https://doi.org/10.1007/978-3-319-15287-5_4
Publisher Name: Springer, Cham
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