# Literature review kernel density estimation

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Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is non-negligible, estimation methods should be adjusted to reduce resulting bias.

## The importance of kernel density estimation bandwidth | Andrey Akinshin

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, including vector quantization. The most basic form of density estimation is a rescaled histogram. We will consider records of the incidence of diabetes.

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Marron, J. Jones, M. Park, B.
Summary and Contributions : Update: I'm raising my score one point in response to the author rebuttal. The paper proposes a kernel method to estimate the PMF of the hard predictions w. Strengths : Common group fairness metrics are computed using hard predictions.
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