Explain the core philosophical difference between Bayesian and frequentist inference. In what real-world ML and product situations would you favor each approach, and what are the practical tradeoffs?
formulate your answer, then —
tldr
Frequentist: probability = long-run frequency; parameters are fixed unknowns; produces p-values and confidence intervals. Bayesian: probability = degree of belief; parameters have distributions; produces posteriors you can directly interpret. Use Bayesian when you have prior knowledge to leverage or small samples. Use frequentist for large-scale A/B testing where organizational familiarity and computational simplicity matter.
follow-up
- What is a conjugate prior? Give an example and explain why they're computationally convenient for Bayesian inference.
- How does Markov Chain Monte Carlo (MCMC) work, and when would you need it instead of analytical posterior computation?
- What is empirical Bayes, and how does it bridge the frequentist and Bayesian approaches in practice?