Dear Erik
The tails will inevitably be long for small datasets as there are few points on which to base this. KDE_Plot when nested in a phase model like:
Sequence("Stratigraphic sequence")
{
Boundary("Start");
KDE_Plot()
{
R_Date("date 1")...
R_Date("date 2")...
R_Date("date 3")...
};
Boundary("End",AD(2000));
};
which is equivalent to:
Sequence("Stratigraphic sequence")
{
Boundary("Start");
Phase()
{
R_Date("date 1")...
R_Date("date 2")...
R_Date("date 3")...
};
Boundary("End",AD(2000));
};
will truncate the KDE distributions a the boundary - so that might help. The following:
Sequence("Stratigraphic sequence")
{
Boundary("Start");
KDE_Plot()
{
Sequence()
{
R_Date("date 1")...
R_Date("date 2")...
R_Date("date 3")...
};
};
Boundary("End",AD(2000));
};
will also work in this way.
Note that KDE_Plot does not affect the marginal posteriors for the dates - or add anything to the model it just generates a KDE distribution for the marginal posteriors. If used in isolation as with the cross reference this KDE distribution is unbounded and will have long tails. If within Boundaries the distribution is allowed to have an abrupt drop (it would be abrupt if the Boundary was very well constrained).
As an example demonstrating the difference of the two approaches look at:
Plot()
{
Sequence("Stratigraphic sequence")
{
Boundary("Start",BC(1600));
KDE_Plot()
{
Sequence()
{
R_Date("date 1",3300,30);
R_Date("date 2",3300,30);
R_Date("date 3",3200,30);
R_Date("date 4",3200,30);
R_Date("date 5",3100,30);
R_Date("date 6",3100,30);
R_Date("date 7",3000,30);
R_Date("date 8",3000,30);
};
};
Boundary("End",BC(1250));
};
KDE_Plot()
{
Date("=date 1");
Date("=date 2");
Date("=date 3");
Date("=date 4");
Date("=date 5");
Date("=date 6");
Date("=date 7");
Date("=date 8");
};
};
Best wishes
Christopher
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