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A Decision Theory Approach to the Approximation of Discrete Probability Densities
January 1980 (vol. 2 no. 1)
pp. 61-67
Dimitri Kazakos, Department of Electrical Engineering, State University of New York at Buffalo, Amherst, NY 14260.
Theodore Cotsidas, Department of Electrical Engineering, State University of New York at Buffalo, Amherst, NY 14260.
The problem of approximating a probability density function by a simpler one is considered from a decision theory viewpoint. Among the family of candidate approximating densities, we seek the one that is most difficult to discriminate from the original. This formulation leads naturaliy to the density at the smallest Bhattacharyya distance. The resulting optimization problem is analyzed in detail.
Citation:
Dimitri Kazakos, Theodore Cotsidas, "A Decision Theory Approach to the Approximation of Discrete Probability Densities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 1, pp. 61-67, Jan. 1980, doi:10.1109/TPAMI.1980.4766971
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