This work proposes a probabilistic extension to Bézier curves as a basis for effectively
modeling stochastic processes with a bounded index set. The proposed stochastic process model
is based on Mixture Density Networks and Bézier curves with Gaussian random variables as
control points. A key advantage of this model is given by the ability to generate multi-mode
predictions in a single inference step thus avoiding the need for Monte Carlo simulation.
