Interactive Tutorials

4. Kernel Density Estimation

1. Causal Models - 2. Causal Structure Learning - 3. Information Theory
   


Problem statement: Given observational data, estimate the underlying probability distribution.

Introduction

    Check also this very gentle introduction to KDE.
Thus, we have a list of data points, by what distribution this data would be generated?
Kernel estimation puts a kernel (for example a Gaussian distribution) on every point and sums it to construct the overall distribution.
Example applet
The rationale is that since only a limited number of points is available, every point is partly uncertain.




1-Dimensional Distributions



2-Dimensional Distributions



last updated: January, 26th 2006 by Jan Lemeire
Parallel Computing Lab, VUB