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Probabilistic Parametric Curves for Sequence Modeling
Ronny Hug
2022
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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.
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Keywords
- Computer science
- Computing & information technology
- Mathematical theory of computation
- Maths for computer scientists
- neural networks
- Neuronale Netzwerke
- Parametric Curves
- Parametrische Kurven
- Probabilistic Sequence Modeling
- Probabilistische Sequenzmodellierung
- Stochastic processes
- Stochastische Prozesse
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
Links
DOI: 10.5445/KSP/1000146434Editions