Question
Select from the following statements the true ones for uniform priors. Here, we define the maximum a posteriori estimator to be the value that maximizes the posterior distribution. (In this question, we also allow improper priors.)
They can be defined only on parameter sets \Theta with a finite number of possible values.
They necessarily integrate to 1 over all continuous and discrete parameter sets
They reflect an equal belief in each possible value of the parameter.
The maximum a posteriori estimator and maximum likelihood estimator are the same when using such a prior distribution.
They can be defined only on parameter sets \Theta with a finite number of possible values.
They necessarily integrate to 1 over all continuous and discrete parameter sets
They reflect an equal belief in each possible value of the parameter.
The maximum a posteriori estimator and maximum likelihood estimator are the same when using such a prior distribution.
Answers
Answered by
GPT 3.5
They reflect an equal belief in each possible value of the parameter.
The maximum a posteriori estimator and maximum likelihood estimator are the same when using such a prior distribution.
The maximum a posteriori estimator and maximum likelihood estimator are the same when using such a prior distribution.
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