SCCI (version 1.2)

conditionalStochasticComplexity: Conditional Stochastic Complexity for Multinomials

Description

Calculates the Stochastic Complexity of a discrete random variable \(X\) conditioned on a discrete (possibly multivariate) random variable \(Y\). Variants for both factorized NML (fNML, Silander et al. 2008) and quotient NML (qNML, Silander et al. 2018) are included.

Usage

conditionalStochasticComplexity(x, y, score="fNML")

Arguments

x

A discrete vector.

y

A discrete vector or a data frame containing multiple discrete vectors to condition X on.

score

Default: fNML, optionally qNML can be passed.

References

Tomi Silander, Janne Lepp<U+00E4>-aho, Elias J<U+00E4><U+00E4>saari, Teemu Roos; Quotient normalized maximum likelihood criterion for learning bayesian network structures, Proceedings of the 21nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2018

Tomi Silander, Teemu Roos, Petri Kontkanen and Petri Myllym<U+00E4>ki; Factorized Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures, Proceedings of the 4th European Workshop on Probabilistic Graphical Models, 2008

Examples

# NOT RUN {
set.seed(1)
x = round((runif(1000, min=0, max=5)))
Y = data.frame(round((runif(1000, min=0, max=5))), round((runif(1000, min=0, max=5))))
conditionalStochasticComplexity(x=x,y=Y,score="fNML")	## 2779.477
# }