# Modelling conditional probabilities with Riemann-Theta Boltzmann Machines

## TL;DR

This paper demonstrates how the conditional probability densities in Riemann-Theta Boltzmann machines can be directly derived from the original model through reparameterization, enabling efficient inference.

## Contribution

It introduces a method to obtain conditional densities in Riemann-Theta Boltzmann machines via reparameterization, simplifying inference procedures.

## Key findings

- Conditional densities are reparameterizations of the original model.
- Direct inference of conditional densities from the Riemann-Theta Boltzmann machine.
- The approach facilitates efficient probabilistic modeling.

## 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.