I’m new to SMC and encountering high rhats when using default settings on a particular model. What knobs would folks suggest playing with to attempt to resolve this? If using MCMC, I’d expand the warmup period and mess with inits for example, but I have no idea what options are available with SMC.
Try incresing the number of draws. This could help not only because you get more samples (as with MCMC) but becuase this also means more particles exploring the posterior.
Another option is to decrease correlation_threshold. Indirectly this increases the number of MCMC steps. Check pymc.smc.sample_smc — PyMC 5.9.1 documentation for details
If that not work you can try runing sample_smc twice and using the output of the first run as the
start argument of the second run. If you do this the computation of the marginal likelihood is no longer valid.