marginal-likelihood-zero-bayes-failure

Summary

When a model assigns zero probability to the data that was actually observed, the Bayesian update breaks down completely because there is no valid posterior to compute. This forces the system to treat the case as a hard failure rather than a routine calculation, since the standard rule for revising beliefs simply cannot produce an answer under those conditions.