Description Author(s) References
The BoltzMM package allows for computation of probability mass functions of fully-visible Boltzmann machines via pfvbm
and allpfvbm
.
Random data can be generated using rfvbm
. Maximum pseudolikelihood estimation of parameters via the MM algorithm can be conducted using fitfvbm
.
Computation of partial derivatives and Hessians can be performed via fvbmpartiald
and fvbmHessian
.
Covariance estimation and normal standard errors can be computed using fvbmcov
and fvbmstderr
.
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.
H.D. Nguyen and I.A. Wood (2016), A block successive lower-bound maximization algorithm for the maximum pseudolikelihood estimation of fully visible Boltzmann machines, Neural Computation, vol 28, pp. 485-492.
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