right at the start: can't thank Martyn enough for the good work that goes into
JAGS; makes MCMC worthwile!!
But still I get problems (that's life):
I'm currently dealing with a slow-mixing chain.. that is, mixing does appear
to improve the longer I run it.. and I've already tried e.g. parameter
expansion to improve mixing..
There are a large number of parameters to be estimated: four parameters each
describe the time series measured in every individual.. a four-dimensional
multivariate normal random effect is used - so not all those parameters are
Still, I would now just like to brute-force this thing into convergence, adapt
for half a million iterations, if necessary.. Computationally, it's feasible.
Problem: very rarely I get an error that the log-likelihood cannot be
calculated, always for a different node.
Is there a way to run an error-tolerant version of "adapt" in rjags.. a way
where errors are just ignored, rather than having the function exit .. after
all, the chain is not used for inference during adaptation.. ?
It may be inconvenient, but I think it is a bad idea to gloss over numerical
problems. Failure to calculate the likelihood indicates a fairly serious
numerical problem. It may be a problem in your model (Gelman's folk theorem)
or a bug in the software that should be fixed.