The BoltzMM package allows for computation of probability mass functions of fully-visible Boltzmann machines via
Random data can be generated using
rfvbm. Maximum pseudolikelihood estimation of parameters via the MM algorithm can be conducted using
Computation of partial derivatives and Hessians can be performed via
Covariance estimation and normal standard errors can be computed using
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.