PyMC3 on AWS EC2

Questions
10

This is what I have running now. But the diagnostics aren’t looking too hot.

with pm.Model() as multinomial_model:
   global_alpha_mean = pm.Normal("global_alpha_mean", mu=0, sd=1)
   global_alpha_sigma = pm.HalfCauchy("global_alpha_sigma", beta=1)
   
   global_beta_mean = pm.Normal("global_beta_mean", mu=0, sd=1)
   global_beta_sigma = pm.HalfCauchy("global_beta_sigma", beta=1)

   #Non centered reparametrization
   alpha_offset = pm.Normal('a_offset', mu=0, sd=1, shape= n_labels)
   local_alphas = pm.Deterministic("local_alphas", global_alpha_mean + alpha_offset * global_alpha_sigma)
   
   #Non centered reparametrization
   beta_offset = pm.Normal('beta_offset', mu=0, sd=1, shape=(n_labels,n_cols))
   local_betas = pm.Deterministic("local_betas", global_beta_mean + beta_offset * global_beta_sigma)
 
   #centered
    #local_alphas = pm.Normal("local_alphas", mu=global_alpha_mean, sd=global_alpha_sigma, shape=n_labels)
    #local_betas = pm.Normal("local_betas", mu=global_beta_memodelan, sd=global_beta_sigma, shape=(n_labels,n_cols))

   mu = local_alphas[labels] + (local_betas[labels] * input_var).sum(axis=1)

   probs = T.nnet.softmax(mu)
   y = pm.Categorical('y', p=probs, observed=target_var)
   #y = pm.Multinomial('y', n=n_counts, p=probs, observed=target_var)

with multinomial_model:
  start=pm.findMAP()
  step = pm.Metropolis()
  trace = pm.sample(10000, step=step, start=start, tune=1000, njobs=4)[2000:10000:5]