BoltzMM: BoltzMM: A package for probability computation, data...

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.

BoltzMM documentation built on May 2, 2019, 11:02 a.m.