Difference between revisions of "Nonparametric Estimators OldKiwi" - Rhea

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-The non-parametric density estimation is
-P(x) = k/(NV)
-where, k is the number of samples in V, N is the total number of samples, and V is the volume surrounding x.
-This estimate is computed by two approaches
-) Parzen window approach
-  - Fixing the volume V and determining the number k of data points inside V
-) KNN(K-Nearest Neighbor)
- - Fixing the value of k and determining the minimum volume V that encompasses k points in the dataset
-* The advantages of non-parametric techniques
- - No assumption about the distribution required ahead of time
- - With enough samples we can converge to an target density
-* The disadvantages of non-parametric techniques
- - If we have a good classification, the number of required samples may be very large
- - Computationally expensive

Revision as of 21:24, 7 April 2008