Asymptotic bias in information estimates and the exponential (Bell) polynomials.
We present a new derivation of the asymptotic correction for bias in the estimate of information from a finite sample. The new derivation reveals a relationship between information estimates and a sequence of polynomials with combinatorial significance, the exponential (Bell) polynomials, and helps to provide an understanding of the form and behavior of the asymptotic correction for bias.
Victor
J D
JD
Department of Neurology and Neuroscience, Weill Medical College of Cornell University, New York City, New York 10021, USA.
D000465:Algorithms; D001185:Artificial Intelligence; D015982:Bias; D008959:Models, Neurological; D011336:Probability