Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
Machine Learning
2020-08-26 v1 Machine Learning
High Energy Physics - Phenomenology
Abstract
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability density function. Therefore the conditional densities can be directly inferred from the Riemann-Theta Boltzmann machine.
Cite
@article{arxiv.1905.11313,
title = {Modelling conditional probabilities with Riemann-Theta Boltzmann Machines},
author = {Stefano Carrazza and Daniel Krefl and Andrea Papaluca},
journal= {arXiv preprint arXiv:1905.11313},
year = {2020}
}
Comments
7 pages, 3 figures, in proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)