Bayesian vs Frequentist Statistics
Two complementary approaches to inference: parameters as random vs fixed, probabilities as belief vs long-run frequency.
📚 Key Differences
- Parameters: random (Bayes) vs fixed (Frequentist)
- Intervals: credible intervals vs confidence intervals
- Evidence: likelihood/prior/posterior vs sampling distribution/p-value
❓ FAQ
1) Which is better?
Context-dependent; each has strengths.
Context-dependent; each has strengths.
2) Can they agree?
With weak priors and large data, results often align.
With weak priors and large data, results often align.
3) Computation?
Bayes may need MCMC/VI; frequentist often closed-form/asymptotic.
Bayes may need MCMC/VI; frequentist often closed-form/asymptotic.