['μ = pm.Normal(\'μ\', mu=0, sd=10)\n    σ = pm.HalfNormal(\'σ\', sd=10)\n    α = pm.Normal(\'α\', mu=0, sd=10)\n    s = pm.HalfNormal(\'s\', sd=10)\n    \n    # Define the growth model\n    growth_rate = α * (X - μ) / s\n    \n    # Define the likelihood\n    y_obs = pm.Normal(\'y_obs\', mu=growth_rate, sd=σ, observed=y)\n    \n    # Sample the posterior\n    trace = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\n    \n# Visualize the posterior\naz.plot_posterior(trace, var_names=[\'α\', \'μ\', \'σ\', \'s\']);\nplt.show()\n\n']