= x_max pm.Potential("likelihood", -k * pm.math.log(n)) # Use NUTS sampler with target_accept=0.9 for discrete variables trace = pm.sample(10000, tune=2000, chains=4)posterior_n = trace.posterior["n"].values.flatten()hdi = az.hdi(trace, var_names=["n"], hdi_prob=0.95)print(f"Posterior mean: {posterior_n.mean():.2f}")print(f"95% HDI: {hdi['n'].values}")"
Administrative reforms will alter how law enforcement agencies in England and Wales document incidents involving perceived bias that don't constitute criminal offenses, aiming to reduce involvement in routine disputes.
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