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If you make use of this material, you may credit the authors as follows:
Hug Ronny, "Probabilistic Parametric Curves for Sequence Modeling", KIT Scientific Publishing, 2022, DOI: 10.5445/KSP/1000146434, License: http://creativecommons.org/licenses/by/4.0
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.
Probabilistische Sequenzmodellierung, Stochastische Prozesse, Neuronale Netzwerke, Parametrische Kurven, Probabilistic Sequence Modeling, Stochastic Processes, Neural Networks, Parametric Curves
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