pfvbm: Probability mass function of a fully-visible Boltzmann...

In BoltzMM: Boltzmann Machines with MM Algorithms

Probability mass function of a fully-visible Boltzmann machine evaluated for an individual vector.

Description

Compute the probability of a string of n>1 binary spin variables (i.e. each element is -1 or 1) arising from a fully-visible Boltzmann machine with some specified bias vector and interaction matrix.

Usage

pfvbm(xval, bvec, Mmat)

Arguments

xval	Vector of length n containing binary spin variables.
bvec	Vector of length n containing real valued bias parameters.
Mmat	Symmetric n by n matrix, with zeros along the diagonal, containing the interaction parameters.

Value

The probability of the random string xval under a fully-visible Boltzmann machine with bias vector bvec and interaction matrix Mmat.

Author(s)

Andrew T. Jones and Hien D. Nguyen

References

H.D. Nguyen and I.A. Wood (2016), Asymptotic normality of the maximum pseudolikelihood estimator for fully-visible Boltzmann machines, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, pp. 897-902.

Examples

# Compute the probability of the vector xval=(-1,1,-1), under bvec and Mmat.
xval <- c(-1,1,-1)
bvec <- c(0,0.5,0.25)
Mmat <- matrix(0.1,3,3) - diag(0.1,3,3)
pfvbm(xval,bvec,Mmat)

BoltzMM documentation built on April 13, 2025, 5:09 p.m.