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Estimation of a Probability Density Function of Very Many Variables

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2 Author(s)
Ichida, Kozo ; Department of Information Science, Kyoto University, Kyoto, Japan. ; Kiyono, Takeshi

The problem of estimating an unknown probability density function from a sequence of samples is well known in pattern classification and many other problems. We approximate the unknown density function by a multivariable spline that is constructed from the histogram of samples. This spline function is expressed as a sum of combinatorially many terms. To assess these numerous terms, the technique of Monte Carlo sampling is exploited and a combined sampling is devised to reduce the standard error.

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Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-5 ,  Issue: 4 )