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Beschreibung
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.
Details
| Verlag | KIT Scientific Publishing |
| Ersterscheinung | 12. Juli 2022 |
| Maße | 21 cm x 14.8 cm |
| Gewicht | 430 Gramm |
| Format | Softcover |
| ISBN-13 | 9783731511984 |
| Auflage | 1. Auflage |
| Seiten | 226 |
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