Examples of frequentist in the following topics:
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- Determine whether the frequentist or subjective approach is better suited for a given situation
- Like most work in the field, the present text adopts the frequentist approach to probability in most cases.
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- A prediction interval bears the same relationship to a future observation that a frequentist confidence interval or Bayesian credible interval bears to an unobservable population parameter.
- Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense -- i.e., an interval $[a, b]$ based on statistics of the sample such that on repeated experiments, $X_{n+1}$ falls in the interval the desired percentage of the time.
- A general technique of frequentist prediction intervals is to find and compute a pivotal quantity of the observables $X_1, \dots, X_n, X_{n+1}$ – meaning a function of observables and parameters whose probability distribution does not depend on the parameters – that can be inverted to give a probability of the future observation $X_{n+1}$ falling in some interval computed in terms of the observed values so far.
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- For users of frequentist methods, various interpretations of a confidence interval can be given.
- For users of frequentist methods, various interpretations of a confidence interval can be given:
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- Statistical hypothesis testing is a key technique of frequentist inference.
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- Bayes' rule tells us how unconditional and conditional probabilities are related whether we work with a frequentist or a Bayesian interpretation of probability.