['μ = pm.Normal(\'μ\', mu=0, sd=10)\n    σ = pm.HalfNormal(\'σ\', sd=10)\n    \n    # Define the likelihood\n    y = pm.Poisson(\'y\', mu=μ + σ, observed=r)\n    \n    # Sample from the posterior\n    trace = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\n\n# Plot the posterior distributions\naz.plot_posterior(trace, var_names=[\'μ\', \'σ\'], hdi_prob=0.95)\nplt.show()\n\n']