BICMoTBF: Computing the BIC score of an MoTBF function

In MoTBFs: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

Computing the BIC score of an MoTBF function

Description

Computes the Bayesian information criterion value (BIC) of a mixture of truncated basis functions. The BIC score is the log likelihood penalized by the number of parameters of the function and the number of records of the evaluated data.

Usage

BICMoTBF(Px, X)

Arguments

Px	A function of class "motbf".
X	A "numeric" vector with the data to evaluate.

Value

A "numeric" value corresponding to the BIC score.

See Also

univMoTBF

Examples

## Data
X <- rexp(10000)

## Data test
Xtest <- rexp(1000)
Xtest <- Xtest[Xtest>=min(X) & Xtest<=max(X)]

## Learning
f1 <- univMoTBF(X, POTENTIAL_TYPE = "MOP", nparam = 10); f1
f2 <- univMoTBF(X, POTENTIAL_TYPE = "MTE", maxParam = 11); f2

## BIC values
BICMoTBF(Px = f1, X = Xtest)
BICMoTBF(Px = f2, X = Xtest)

MoTBFs documentation built on April 18, 2022, 5:06 p.m.