This is the talk page for the slecture notes on Parzen Window Density Estimation. Please leave me a comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.

Questions and Comments

• This slecture has a very good and succinct introduction and overview of parametric and nonparametric density estimation methods, and clearly states why nonparametric density estimation methods are needed in practice.
• It also nicely shows the basic principles for Parzen Window method and explains the underlying assumptions and conditions behind the equation.

• It well described the experiment of the Parzen Window method with a hypercube window function with edge length $ h $. And it showed how much the method can be sensitive to the choice of $ h $.

• It could have been better if the slecture compared the Parzen Window method with different number of samples (training set sizes).
• Even though the slecture mentions about the popular Gaussian window function, there is no detailed discussion about the choice of different window functions and their density estimation results.
• Since we cannot visualize the higher dimension than 3D feature space, it can be improved by having classification results to compare different sample sizes and the parameter $ h $.

Reviewed by Keehwan Park

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