I noticed that the fit of code 14.26 (the instrumental variable problem) in the Statistical Rethinking numpyro translation (Chapter 14. Adventures in Covariance | Statistical Rethinking (2nd ed.) with NumPyro) has issues with very large r hat values, and the parameter estimates don’t match the textbook closely. Is this a known limitation of numpyro? Or are there other ways to do it within the framework?

```
mean std median 5.5% 94.5% n_eff r_hat
Rho[0,0] 1.00 0.00 1.00 1.00 1.00 nan nan
Rho[0,1] 0.11 0.64 0.45 -1.00 0.53 2.01 14.83
Rho[1,0] 0.11 0.64 0.45 -1.00 0.53 2.01 14.83
Rho[1,1] 1.00 0.00 1.00 1.00 1.00 nan 1.00
Sigma[0] 287.87 497.01 1.01 0.95 1148.57 2.00 8007.16
Sigma[1] 1229.12 2128.10 0.79 0.76 4914.21 2.00 107101.21
aE -0.34 0.59 -0.01 -1.37 0.04 2.01 20.67
aW -1.27 2.19 -0.02 -5.06 0.05 2.00 60.57
bEW -1.31 2.32 -0.00 -5.33 0.10 2.00 41.73
bQE 1.40 1.35 0.63 0.59 3.73 2.00 49.61
Number of divergences: 0
```