Hi all,
I was looking at some of my results recently and I noticed that on some of my runs that take a long time to converge I have walkers stuck in low probability space and was wondering how to deal with that.
I have attached a picture of what this looks like. In this one some of the chains found their way back eventually and some did not.
for the moves I am using 10% DEsnooker, 81% DEmove and 9% DEmove with gamma = 1.0 (from the paper linked from emcee)
My cost function is expensive and can take a minute or more to evaluate
I am using an empirical error model using a Kernel Density Estimator since my errors are not random, independent and normally distributed. I think walkers get trapped because of this because I think they end up trapped in tiny fluctuations of the kernel density estimator.
What I was thinking of doing is take any chain that has spent more than about 10 steps in a low probability region and kick it back to a high probability region. My first thought on that is that any walker more than about 4 orders of magnitude below the highest probability is probably in too low of a probability region. In the end I really only care about 5-95 CI on each variable and maybe 1-99 CI under some circumstances.
Any thoughts on this or other ideas on how to solve this problem?
Thanks