Update visual-mcmc-diagnostics.Rmd

vignettes/visual-mcmc-diagnostics.Rmd

@@ -154,7 +154,7 @@ schools_mod_ncp <- stan_model("schools_mod_ncp.stan")
 We then fit the model by calling Stan's MCMC algorithm using the `sampling` 
 function (the increased `adapt_delta` param is to make the sampler a bit more "careful" and avoid false positive divergences),
 ```{r fit-models-hidden, results='hide', message=FALSE}
-fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214053, control = list(adapt_delta = 0.9))
+fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214054, control = list(adapt_delta = 0.9))
 fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9))
 ```
 and extract a `iterations x chains x parameters` array of posterior draws with