Having uninitialized nodes in a nimbleModel can potentially cause some algorithms to fail, and can lead to poor performance in others. Here are some general guidelines on how non-intitialized variables can affect performance:
MCMC will atuo-initialize, but will do so from the prior distribution. This can cause slow convergence, especially in the case of diffuse priors.
Likewise, particle filtering methods will initialize top-level parameters from their prior distributions, which can lead to errors or poor performance in these methods.