Transcription of L7: Kernel density estimation - Texas A&M University
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L7: Kernel density estimation - Texas A&M University
L7: Kernel density estimation Non-parametric density estimation Histograms Parzen windows Smooth kernels Product Kernel density estimation The na ve Bayes classifier CSCE 666 Pattern Analysis | Ricardo Gutierrez-Osuna | CSE@TAMU 1. Non-parametric density estimation In the previous two lectures we have assumed that either The likelihoods ( | ) were known (LRT), or At least their parametric form was known (parameter estimation ). The methods that will be presented in the next two lectures do not afford such luxuries Instead, they attempt to estimate the density directly from the data without assuming a particular form for the underlying distribution Sounds challenging? You bet! 7* P(x1, x2| wi). 7. 777. 7777*. 7 77. 77*. 7*. 77. x2 9 9* non-parametric 0 9999*. 99995 55559. 8989*. 8885. 55*. 889*. 95* 555*22. 59* density estimation 8*. 33*8. 3* 8*. 883* 5*. 55 2*. 5 2222*. 2* 10*. 8* 22 22*. 2 10*. 33. 33*. 8. 3 3. 3*. 3 3* 2*.
CSCE 666 Pattern Analysis | Ricardo Gutierrez-Osuna | CSE@TAMU 4 • The histogram is a very simple form of density estimation, but has several drawbacks –The density estimate depends on the starting position of the bins
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