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

Description Usage Arguments Value Author(s) References Examples

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

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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

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# 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 May 2, 2019, 11:02 a.m.