Frequentist vs Bayesian — key philosophical difference.

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Answer

$Frequentist: parameters are fixed but unknown; probability = long-run frequency; CIs, p-values, MLE.$
$Bayesian: parameters are random with a distribution encoding belief; probability = degree of belief; posterior, credible intervals, MAP.$
$Pragmatic: Bayesian is natural when priors are meaningful, uncertainty quantification matters, or you're doing online learning; frequentist is faster and standard in confirmatory studies.$
$Modern ML mixes both freely (MAP = MLE + regularization; Bayesian deep learning).$

Check yourself — multiple choice

Same thing
Frequentist: fixed param + long-run frequency; Bayesian: random param + belief; modern ML mixes both (MAP = MLE + regularization)
Only frequentist
Only Bayesian

Frequentist: param fixed, prob = long-run; Bayesian: param random, prob = belief.

#bayesian#theory

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