Models, code, and papers for "Andrea Papaluca":

Modelling conditional probabilities with Riemann-Theta Boltzmann Machines

May 27, 2019
Stefano Carrazza, Daniel Krefl, Andrea Papaluca

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.

* 7 pages, 3 figures, in proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019) 

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