### 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.