discretizeMDL: Discretize a real valued distribution

In miic: Learning Causal or Non-Causal Graphical Models Using Information Theory

Description

This function performs minimum description length (MDL)-optimal histogram density estimation as described in Kontkanen and Myllymäki (2007) and returns the cutpoints found to give the best model according to the MDL principle.

Usage

discretizeMDL(x = NULL, max_bins = 20)

Arguments

x	[a vector] A vector that contains the distribution to be discretized.
max_bins	[an int] The maximum number of bins allowed by the algorithm.

Value

A list containing the cutpoints of the best discretization.

References

• Kontkanen P, Myllymäki P. MDL histogram density estimation. Artificial Intelligence and Statistics 2007 Mar 11 (pp. 219-226).

Examples

library(miic)
# Bimodal normal distribution
N <- 300
modes <- sample(1:2, size = N, replace = TRUE)
x <- as.numeric(modes == 1) * rnorm(N, mean = 0, sd = 1) +
     as.numeric(modes == 2) * rnorm(N, mean = 5, sd = 2)
MDL_disc <- discretizeMDL(x)
hist(x, breaks = MDL_disc$cutpoints)

N <- 2000
modes <- sample(1:2, size = N, replace = TRUE)
x <- as.numeric(modes == 1) * rnorm(N, mean = 0, sd = 1) +
     as.numeric(modes == 2) * rnorm(N, mean = 5, sd = 2)
MDL_disc <- discretizeMDL(x)
hist(x, breaks = MDL_disc$cutpoints)

miic documentation built on Jan. 13, 2021, 10:34 a.m.