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:
“... it would be impossible for women to stand in higher estimation than they do here. The deference that is paid to them at all times and in all places has often occasioned me as much surprise as pleasure.”
—Frances Wright (17951852)
“The importance to the writer of first writing must be out of all proportion of the actual value of what is written.”
—Elizabeth Bowen (18991973)