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In probability and statistics,
density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.
A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques.
Here is an illustration of density estimation.
References
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York: Springer, 2001. ISBN 0-387-95284-5. (See Chapter 6.)
- D.W. Scott. Multivariate Density Estimation. Theory, Practice and Visualization. New York: Wiley, 1992.
- B.W. Silverman. Density Estimation. London: Chapman and Hall, 1986.
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