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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.

CSCE 666 Pattern Analysis | Ricardo Gutierrez-Osuna | CSE@TAMU 3 The histogram • The simplest form of non-parametric DE is the histogram –Divide the sample space into a …

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  University, Texas, Density, Kernel, Estimation, Texas a amp m university, Tamus, Kernel density estimation

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