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)

    The chimney is to some extent an independent structure, standing on the ground, and rising through the house to the heavens; even after the house is burned it still stands sometimes, and its importance and independence are apparent.
    Henry David Thoreau (1817–1862)