CmdStan Guide now online

[mitzimorris]

@mike-lawrence

4.4: what’s going on with the ridiculously high Rhat for stepsize__?

good question. I don’t think the Rhat statistic makes sense for stepsize, but I’m curious as to why it has the value that it does. pinging @avehtari - what is the correct thing to report? don’t analyze sample state columns in Stan csv output file?

@mike-lawrence

generated quantities: is this section incomplete?

yes. working on it now.

@mike-lawrence

“Some users familiar with older approaches to MCMC sampling might be used to thinning to eliminate an expected autocorrelation in the samples. HMC is not nearly as susceptible to this autocorrelation problem and thus thinning is generally not required nor advised except in circumstances where storage of the samples is limited and/or RAM for later processing the samples is limited.”

excellent suggestion - will add - many thanks!

@mike-lawrence

9.2.1 “Raising the value of delta will also allow some models that would otherwise get stuck to overcome their blockages” & also this from 16.3.1: “If the divergent transitions cannot be eliminated by increasing the adapt_delta parameter, we have to find a different way to write the model that is logically equivalent but simplifies the geometry of the posterior distribution.” → This seems to imply that the first step in troubleshooting divergences is to change adapt_delta, but I thought it was becoming more strongly recommended that folks not do this, and instead first look to changing the model geometry, which could yield both more efficient and faster sampling.

I could move or copy the discussion from 9.5.6 (Redirecting…) to 9.2.1.
note that not long ago, this was disussed elsewhere - Improve warnings for low ESS - at which point Bob said:

@Bob_Carpenter

I’ve found it to help a lot with hierarchical models. So much so I might suggest we just increase the adaptation target. But that’s a different thread :-)

@mike-lawrence

what happens if you specify the random seed and id as ${i} ? Would this thwart the in-built mechanisms for avoiding overlap in seeds?

short answer - no - each chain will be running off an RNG which has its own seed.
the samplers use both the seed and the id together to create the RNG used by the sampler - https://github.com/stan-dev/stan/blob/develop/src/stan/services/util/create_rng.hpp

many thanks for the close read and good suggestions!