query: Why is it sufficient to minimize expected prediction error (EPE) pointwise?
positive_passages.docid: 148004
positive_passages.text: Expected prediction error - derivation
negative_passages.docid: 132392
negative_passages.text: Properties of Expectation
negative_passages.docid: 131146
negative_passages.text: Create an adaptive amount of local variables for error propagation
negative_passages.docid: 49877
negative_passages.text: Expected value and indicator random variable
negative_passages.docid: 20178
negative_passages.text: Ljung-Box finite sample adjustments
negative_passages.docid: 84076
negative_passages.text: Basic intuition about minimal sufficient statistic
negative_passages.docid: 27788
negative_passages.text: Why is the expected value $E(X^2) \neq E(X)^2$?
negative_passages.docid: 91232
negative_passages.text: Pointwise convergent and total variation
negative_passages.docid: 45751
negative_passages.text: Expectation of the maximum of i.i.d. geometric random variables
negative_passages.docid: 27632
negative_passages.text: What is an intuitive explanation of the Expectation Maximization technique?
negative_passages.docid: 57850
negative_passages.text: Bounding an Error on the inverse.
negative_passages.docid: 16209
negative_passages.text: Importance of optimizing the correct loss function
negative_passages.docid: 125934
negative_passages.text: Minimize absolute values of errors instead of squares
negative_passages.docid: 17893
negative_passages.text: How do I show that the mean of the posterior density minimizes this squared error loss function?
negative_passages.docid: 12508
negative_passages.text: Efficient way to generate random points with a predefined lower bound on their pairwise Euclidean distance
negative_passages.docid: 1094
negative_passages.text: Expected Value of the maximum of two exponentially distributed random variables
negative_passages.docid: 101724
negative_passages.text: Expected minimum distance from a point with varying density