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.
pfvbm(xval, bvec, Mmat)
Vector of length n containing binary spin variables.
Vector of length n containing real valued bias parameters.
Symmetric n by n matrix, with zeros along the diagonal, containing the interaction parameters.
The probability of the random string
xval under a fully-visible Boltzmann machine with bias vector
bvec and interaction matrix
Andrew T. Jones and Hien D. Nguyen
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.
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