arXiv:1905.11313·v1·Machine Learning
Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
Stefano Carrazza🇮🇹 · Daniel Krefl🇨🇭 · Andrea Papaluca🇮🇹
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.
Comments: 7 pages, 3 figures, in proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)