bnlearn (version 5.0.1)

information theoretic quantities: Compute the distance between two fitted Bayesian networks

Description

Compute Shannon's entropy of a fitted Bayesian network and the Kullback-Leibler divergence between two fitted Bayesian networks.

Usage

H(P)
KL(P, Q)

Value

H() and KL() return a single numeric value.

Arguments

P, Q

objects of class bn.fit.

Author

Marco Scutari

Examples

if (FALSE) {
# discrete networks
dag = model2network("[A][C][F][B|A][D|A:C][E|B:F]")
fitted1 = bn.fit(dag, learning.test, method = "mle")
fitted2 = bn.fit(dag, learning.test, method = "bayes", iss = 20)

H(fitted1)
H(fitted2)

KL(fitted1, fitted1)
KL(fitted2, fitted2)
KL(fitted1, fitted2)
}

# continuous, singular networks.
dag = model2network("[A][B][E][G][C|A:B][D|B][F|A:D:E:G]")
singular = fitted1 = bn.fit(dag, gaussian.test)
singular$A = list(coef = coef(fitted1[["A"]]) + runif(1), sd = 0)

H(singular)
H(fitted1)

KL(singular, fitted1)
KL(fitted1, singular)