Issues with Truncated Normal

Questions version agnostic
9

This seems to be doing better. I changed the model to the following

coords = {'cann':cann}
with pm.Model(coords = coords) as cannibal_model:
   cannibal = pm.Data('cannibal', cann_idx, mutable = True)
   obs = pm.Data('obs', obs_array, mutable = True)

   #  beta = pm.Normal('beta', mu=0, sigma = .1)
   alpha = pm.Normal('alpha', mu=.99, sigma = .07, dims = ['cann'])
   sigma = pm.HalfNormal('sigma', 1)
   y_latent = pm.Normal.dist(mu=alpha[cannibal], sigma = sigma)
    


   eaches = pm.Censored('predicted_eaches',
                           dist=y_latent,
                           lower = 0,
                           upper = 1,
                           observed=obs)

   idata = pm.sampling_jax.sample_numpyro_nuts(draws = 1000, tune=2000, target_accept = .95)

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I think this is a good base to expand out from. Thank you for the help.