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@cmccoy just found that adding extra MH steps at each generation can considerably improve the solutions. this is sweet and it would be nice to have some means to figure out how many to add, e.g. where do we reach the point of diminishing returns? One way to do this would be to log the logL after each MH step to our log file and eyeball it for a handful of datasets. Another might be to have some kind of automated convergence diagnostic that can detect when MH steps are no longer improving the logL by much.
The text was updated successfully, but these errors were encountered:
At one point you brought up BEAST-style adjustable parameter move, where the size of the move is modified to generate the desired acceptance rate. That might be good to revisit here?
One downside of this would be that desired move sizes early (with lots of leaf joins) might not match those desired late.
@cmccoy just found that adding extra MH steps at each generation can considerably improve the solutions. this is sweet and it would be nice to have some means to figure out how many to add, e.g. where do we reach the point of diminishing returns? One way to do this would be to log the logL after each MH step to our log file and eyeball it for a handful of datasets. Another might be to have some kind of automated convergence diagnostic that can detect when MH steps are no longer improving the logL by much.
The text was updated successfully, but these errors were encountered: