When drawing pggg parameters I tend to get a limited (0.5 %) cluster of cases with mean k's between 985 and 999, with absolutely no mean k between 50 and 985 (Expected aggregate k is around 0.8).
The only thing seemingly setting these cases apart is that they're somewhat regular but rather short-lived, they're probably somewhat overrepresented and must be confusing the algorithm.
The upper bound for k slice sampling is set at 1000, which at first sight I don't think is a realistic expectation in any scenario? I would suspect a limit of around 100 to be safer and would adjust the algorithm accordingly.
Speaking of assumptions, it occurred to me that k's aggregate distribution is more likely to follow a lognormal than a gamma distribution. Even in the clumpiest of scenarios, extremely low k's remain less likely than values around 0.5, with a few higher k cases always remaining quite likely, a situation the gamma distribution doesn't allow for.
When drawing pggg parameters I tend to get a limited (0.5 %) cluster of cases with mean
k's between 985 and 999, with absolutely no meankbetween 50 and 985 (Expected aggregatekis around 0.8).The only thing seemingly setting these cases apart is that they're somewhat regular but rather short-lived, they're probably somewhat overrepresented and must be confusing the algorithm.
The upper bound for
kslice sampling is set at 1000, which at first sight I don't think is a realistic expectation in any scenario? I would suspect a limit of around 100 to be safer and would adjust the algorithm accordingly.Speaking of assumptions, it occurred to me that
k's aggregate distribution is more likely to follow a lognormal than a gamma distribution. Even in the clumpiest of scenarios, extremely lowk's remain less likely than values around 0.5, with a few higherkcases always remaining quite likely, a situation the gamma distribution doesn't allow for.