Estimation Via Importance Sampling
Consider the general problem of estimating the quantity, where is some performance function and is a member of some parametric family of distributions. Using importance sampling this quantity can be estimated as, where is a random sample from . For positive, the theoretically optimal importance sampling density (pdf)is given by . This, however, depends on the unknown . The CE method aims to approximate the optimal pdf by adaptively selecting members of the parametric family that are closest (in the Kullback-Leibler sense) to the optimal pdf .
Read more about this topic: Cross-entropy Method
Famous quotes containing the words estimation and/or importance:
“A higher class, in the estimation and love of this city- building, market-going race of mankind, are the poets, who, from the intellectual kingdom, feed the thought and imagination with ideas and pictures which raise men out of the world of corn and money, and console them for the short-comings of the day, and the meanness of labor and traffic.”
—Ralph Waldo Emerson (18031882)
“Whoever deliberately attempts to insure confidentiality with another person is usually in doubt as to whether he inspires that persons confidence in him. One who is sure that he inspires confidence attaches little importance to confidentiality.”
—Friedrich Nietzsche (18441900)