I have a model of the form

```
x0 ~ normal(-1, 1);
for (t in 1:(T-1))
z[t+1] ~ normal(f(z[t], x0), sig);
data ~ normal(z, eps);
```

where HMC shows Rhat values near 1 for `x0`

& high Rhat values for `z`

, but I only care about the `x0`

parameter.

Is it correct to assume that because the Rhat of `z`

is poor, and that the only link between `data`

and `x0`

is `z`

, that the estimate of `x0`

is bad despite its Rhat being low?

After rereading the section of BDA on Rhat (and a bunch of forum posts), that alternative interpretation I thought of is it, running the chains longer would improve estimate of `z`

, but not `x0`

… but I can’t make heads or tails of that given the dependencies of the variables in this model. Any words of wisdom are welcome.