Cross-entropy Method - Estimation Via Importance Sampling

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:

    No man ever stood lower in my estimation for having a patch in his clothes; yet I am sure that there is greater anxiety, commonly, to have fashionable, or at least clean and unpatched clothes, than to have a sound conscience.
    Henry David Thoreau (1817–1862)

    A man’s personal defects will commonly have with the rest of the world precisely that importance which they have to himself. If he makes light of them, so will other men.
    Ralph Waldo Emerson (1803–1882)