Density estimation
![Demonstration of density estimation using kernel smoothing: The true density is mixture of two Gaussians centered around 0 and 3, shown with solid blue curve. In each frame, 100 samples are generated from the distribution, shown in red. Centered on each sample, a Gaussian kernel is drawn in gray. Averaging the Gaussians yields the density estimate shown in the dashed black curve.](/Images/godic/202501/08/KernelDensityGaussianAnimated0104.gif")
![diabetes=0) (blue), and p (glu) (black)](/Images/godic/202501/08/P_glu_given_diabetes0105.png")
![glu)](/Images/godic/202501/08/P_diabetes_given_glu0105.png")
![glu)](/Images/godic/202501/08/Glu_opt0105.png")
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.