KL_div {YEAB} | R Documentation |
Computes the Kullback-Leibler divergence based on kernel density estimates
Description
Computes the Kullback-Leibler divergence based on kernel density estimates of two samples.
Usage
KL_div(x, y, from_a, to_b)
Arguments
x |
numeric, the values from a sample p |
y |
numeric, the values from a sample q |
from_a |
numeric, the lower limit of the integration |
to_b |
numeric, the upper limit of the integration |
Details
The Kullback-Leibler divergence is defined as
D_{KL}(P||Q) = \int_{-\infty}^{\infty} p(x) \log \frac{p(x)}{q(x)} dx
Value
a numeric value that is the kl divergence
Examples
set.seed(123)
p <- rnorm(100)
q <- rnorm(100)
KL_div(p, q, -Inf, Inf) # 0.07579204
q <- rnorm(100, 10, 4)
KL_div(p, q, -Inf, Inf) # 7.769912
[Package YEAB version 1.0.6 Index]