n_divergent
Quick definition
The number of leapfrog transitions with diverging error. Because NUTS terminates
at the first divergence this will be either 0 or 1 for each iteration.
The average value of n_divergent
over all iterations is therefore
the proportion of iterations with diverging error.
More details
Stan uses a symplectic integrator to approximate the exact solution of the
Hamiltonian dynamics and when stepsize
is too large relative
to the curvature of the log posterior this approximation can diverge and
threaten the validity of the sampler. n_divergent
counts the
number of iterations within a given sample that have diverged and any
non-zero value suggests that the samples may be biased in which case
the step size needs to be decreased. Note that, because sampling is
immediately terminated once a divergence is encountered, n_divergent
should be only 0 or 1.
If there are any post-warmup iterations for which n_divergent = 1
then the results may be biased and should not be used. You should try rerunning
the model with a higher target acceptance probability (which will decrease the
step size) until n_divergent = 0
for all post-warmup iterations.