kld_est_discrete: Plug-in KL divergence estimator for samples from discrete...

In kldest: Sample-Based Estimation of Kullback-Leibler Divergence

Plug-in KL divergence estimator for samples from discrete distributions

Description

Plug-in KL divergence estimator for samples from discrete distributions

Usage

kld_est_discrete(X, Y = NULL, q = NULL)

Arguments

X, Y	n-by-d and m-by-d matrices or data frames, representing n samples from the true discrete distribution P and m samples from the approximate discrete distribution Q, both in d dimensions. Vector input is treated as a column matrix. Argument Y can be omitted if argument q is given (see below).
q	The probability mass function of the approximate distribution Q. Currently, the one-sample problem is only implemented for d=1.

Value

A scalar, the estimated Kullback-Leibler divergence \hat D_{KL}(P||Q).

Examples

# 1D example, two samples
X <- c(rep('M',5),rep('F',5))
Y <- c(rep('M',6),rep('F',4))
kld_est_discrete(X, Y)

# 1D example, one sample
X <- c(rep(0,4),rep(1,6))
q <- function(x) dbinom(x, size = 1, prob = 0.5)
kld_est_discrete(X, q = q)

kldest documentation built on May 29, 2024, 3 a.m.