A new class of entropy estimators for multi-dimensional densities Export

@proceedings{citeulike:2456451, abstract = {We present a new class of estimators for approximating the entropy of multi-dimensional probability densities based on a sample of the density. These estimators extend the classic "m-spacing" estimators of Vasicek (1976) and others for estimating entropies of one-dimensional probability densities. Unlike plug-in estimators of entropy, which first estimate a probability density and then compute its entropy. our estimators avoid the difficult intermediate step of density estimation. For fixed dimension. the estimators an polynomial in the sample size. Similarities to consistent and asymptotically efficient one-dimensional estimators of entropy suggest that our estimators may sham these properties.}, author = {Miller, E. G.}, booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on}, citeulike-article-id = {2456451}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICASSP.2003.1199463}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1199463}, doi = {10.1109/ICASSP.2003.1199463}, journal = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on}, keywords = {entropy-estimation}, pages = {III--297-300 vol.3}, posted-at = {2008-03-02 04:50:50}, priority = {3}, title = {A new class of entropy estimators for multi-dimensional densities}, url = {http://dx.doi.org/10.1109/ICASSP.2003.1199463}, volume = {3}, year = {2003} }

TY - CONF ID - citeulike:2456451 L3 - citeulike-article-id:2456451 N2 - We present a new class of estimators for approximating the entropy of multi-dimensional probability densities based on a sample of the density. These estimators extend the classic "m-spacing" estimators of Vasicek (1976) and others for estimating entropies of one-dimensional probability densities. Unlike plug-in estimators of entropy, which first estimate a probability density and then compute its entropy. our estimators avoid the difficult intermediate step of density estimation. For fixed dimension. the estimators an polynomial in the sample size. Similarities to consistent and asymptotically efficient one-dimensional estimators of entropy suggest that our estimators may sham these properties. JF - Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on EP - 297-300 vol.3 TI - A new class of entropy estimators for multi-dimensional densities VL - 3 T2 - Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on SP - III KW - entropy-estimation AU - Miller, EG PY - 2003/// UR - http://dx.doi.org/10.1109/ICASSP.2003.1199463 ER -