Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:abbreviation |
gptkb:KDE
|
| gptkbp:advantage |
Sensitive to bandwidth selection
Produces smooth estimate |
| gptkbp:alternativeTo |
Histogram
|
| gptkbp:application |
Data visualization
Clustering Anomaly detection Density-based outlier detection |
| gptkbp:category |
Non-parametric estimation
Smoothing technique |
| gptkbp:common_kernel |
Gaussian kernel
Epanechnikov kernel Triangular kernel Uniform kernel |
| gptkbp:field |
gptkb:Machine_Learning
Statistics Data Analysis |
| https://www.w3.org/2000/01/rdf-schema#label |
Kernel Density Estimation
|
| gptkbp:implementedIn |
gptkb:MATLAB
Python (scipy.stats, seaborn) R (density function) |
| gptkbp:input |
Sample data
|
| gptkbp:introduced |
gptkb:Emanuel_Parzen
gptkb:Murray_Rosenblatt 1956 |
| gptkbp:mathematical_formula |
\\hat{f}_h(x) = \\frac{1}{n h} \\sum_{i=1}^n K\\left(\\frac{x - x_i}{h}\\right)
|
| gptkbp:output |
Smooth probability density estimate
|
| gptkbp:parameter |
Bandwidth (h)
Kernel function (K) |
| gptkbp:relatedConcept |
gptkb:Parzen_window
Density estimation Mean shift clustering Probability density function Cross-validation Bias-variance tradeoff Adaptive kernel density estimation Bandwidth matrix Bandwidth selection Cumulative distribution function Multivariate kernel density estimation Plug-in estimator Silverman's rule of thumb Smoothing parameter |
| gptkbp:relatedTo |
Histogram
Non-parametric statistics |
| gptkbp:usedFor |
Estimating probability density function
|
| gptkbp:uses |
Kernel function
Bandwidth parameter |
| gptkbp:bfsParent |
gptkb:Gaussian_Function
gptkb:Kernel_Methods |
| gptkbp:bfsLayer |
8
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