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I've heard a couple of definitions of a Bayesian plot, but I am not 100% sure what it is and whether it is possible to plot this in an R.

An example of the plot is here: http://www.springerimages.com/Images/MedicineAndPublicHealth/1-10.1007_s11307-008-0154-3-1

In this particular example there are four different tests, the prior probability is evaluated based on the proven probability of cancer recurrence based on 7 patients. This is compared against varying prevalences of tumour (As far as I can understand this is the the x-axis reflects these changing prevalences). The y-axis is the posterior probability. What I don't fully understand is what the likelihood is and how they get it from the different methods to actually calcualte the posterior and show there are differences between these methods.

I am not necessarily interested in understanding this particular example. I just want to understand how I can construct and interpret this plot on my own data. I understand that my prior should be my disease prevalence, but what I don't understand is how the likelihood should be represented in this plot (is it the x-axis?). From my own data I can evaluate the posterior because I know the prior and the likelihood... I just don't know how to put them into this plot.

Has anyone made such a plot for their own work that can help me to understand the intuition?

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