Hi Numpyro fans,

I have a 21 parameters sampling to manage and I am using Numpyro NUTS with a model consisting of Uniform priors for same variables and Normal priors for the others, and finally the likelihood is a MultivariateNormal(signal, cov) distribution where cov is a constant covariance matrix 2250 x 2250. Now,

I use a simple running

nuts_kernel = numpyro.infer.NUTS(cond_model,

init_strategy=numpyro.infer.init_to_sample(),

max_tree_depth=5) # max_tree_depth=10

mcmc = numpyro.infer.MCMC(nuts_kernel,

num_warmup=1_000,

num_samples=8_000,

num_chains=1,

jit_model_args=True,

progress_bar=False)

The job summary indicates that r_hat is 1.00-1.01 while the n_eff for some variables are O(150) but some most are about 50 which is rather low effeiciency.

I have identified that 5 variables are highly correlated.

Do you have some advises to make the sampling more efficient? what about the mass matrix shaping for instance ?