BoltzMM (version 0.1.5)

pfvbm: 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)

Value

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

Author

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)