Explain MCMC and Metropolis-Hastings: why does the chain sample from the posterior?
Bayesian inference requires an intractable normalizing constant, and MCMC works around it. The signal is explaining the acceptance ratio, detailed balance, and why you can drop the constant entirely. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Bayesian inference requires an intractable normalizing constant, and MCMC works around it. The signal is explaining the acceptance ratio, detailed balance, and why you can drop the constant entirely. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
