Hi Gianmarco,
The converse (Tau_Boundary:Boundary) is often used in archaeological situations where you have a destruction layer, indicating that the majority of 14C ages are likely to closely predate the destruction, but with a decreasing probability of dating older material.
In an environmental context the Tau_Boundary & Boundary combination (both ways around) was applied by, e.g., Vandergoes et al. 2013 (https://doi.org/10.1016/j.quascirev.2012.11.006), to account for 14C analyses of plant macrofossils dated immediately below and immediately above their tephra layer of interest, accounting for the likely close chronological relationship between the 14C-dated materials and the date of the volcanic eruption, but acknowledging that there would be a decreasing probability of the 14C ages falling significantly earlier and later than the eruption. The same was also done by Egan et al. 2015 (https://doi.org/10.1177/0959683615576230).
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Cheers Gianmarco,
There is also the Zero_Boundary<->Boundary combination which gives you a linear rise/fall, rather than the exponential rise fall (using Tau_Boundary<->Boundary) or the Gaussian rise/fall (Sigma_Boundary<->Boundary).
I guess the important thing is to be able to justify (based upon your archaeological assumptions) why any of those distributions is the most likely (as you have started to describe below), and build that in to your model prior.
You can (and probably should!) run a series of models, using those different model configurations and assess what difference it makes to your event(s)/parameter(s) of interest (presumably here, the ultimate site abandonment). It may well be that the output for that event is relatively insensitive to the choice, which you can argue as giving robustness to the quoted age range.
I can imagine (based on my own experience!) that it could be hard to be certain as to which of those three rising/decreasing Phase distributions (the result of the Boundary combinations) is ultimately 'correct'.
I guess too that you could also use, e.g., the model agreement indices to see which configuration better matches your data. Though this might not be a foolproof assessment, especially if you only have a relatively limited number of 14C dates.
Good luck!
Richard