@book{Hug2022, abstract = {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. Umfang: XII, 194 S. Preis: 44.00 €}, address = {Karlsruhe}, author = {Hug, Ronny}, doi = {10.5445/KSP/1000146434}, isbn = {978-3-7315-1198-4}, keyword = {Probabilistische Sequenzmodellierung, Stochastische Prozesse, Neuronale Netzwerke, Parametrische Kurven, Probabilistic Sequence Modeling, Stochastic Processes, Neural Networks, Parametric Curves}, month = {Jul}, pages = {226}, publisher = {KIT Scientific Publishing}, title = {Probabilistic Parametric Curves for Sequence Modeling}, year = {2022} }