Frequentist vs. Bayesian I
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Which of the following are aspects of Bayesian modeling approach, as opposed to the frequentist modeling approach? (Choose all that apply. Refer to the slides.)
In Bayesian statistics, the true parameter is modeled as a random variable, or at the very least, the uncertainty regarding the true parameter is modelled as such.
In most practical applications of Bayesian statistics, we are trying to estimate the true parameter only from the observation data and our chosen model.
In Bayesian statistics, we use the data to update our prior belief about a parameter and transform it into a posterior belief, which is reflected by a posterior distribution.
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Factors that Can be Specified in the Frequentist View
1 point possible (graded)
Suppose that we have some background information about our statistical problem, say from intuition or existing literature. We want to stick to a frequentist approach, but wish to specify our problem so that the outcome would be more in line with what we know so far. Which of the following components are we allowed to specify? (Choose all that apply.)
The set possible parameters
The probability model
A distribution by which we weight the likelihood
The procedure by which we would infer or estimate the true parameter based on the observations and model. (MLE, method of moments, M-estimator, etc.)