Bayesian vs frequentist — what's the core difference?
mediumAnswer
- Frequentists treat parameters as fixed unknowns and estimate them via long-run frequency properties (unbiased estimators, confidence intervals, p-values).
- Bayesians treat parameters as random variables with a prior distribution, update with data via Bayes' rule to get a posterior, and summarize with credible intervals.
- Bayesian methods handle prior knowledge naturally and give posterior probability statements directly.
Check yourself — multiple choice
- Bayesians treat parameters as fixed
- Bayesians update a prior distribution to a posterior via Bayes' rule
- Frequentist CIs give direct probability statements about parameters
- There is no practical difference
Bayesian: .
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