On the Samplings of Gaussian Mixture
- Forschungsthema/Bereich
- Artificial Intelligence
- Typ der Abschlussarbeit
- Bachelor / Master
- Startzeitpunkt
- -
- Bewerbungsschluss
- 30.06.2028
- Dauer der Arbeit
- 4 months(BSc) - 6 months(MSc)
Beschreibung
Monte Carlo sampling uses randomness to estimate numerical results, often for integration or simulation. Deterministic sampling follows fixed rules, producing repeatable outputs. Las Vegas algorithms are randomized but always return correct results, though runtime varies. These methods differ in accuracy, reliability, and efficiency depending on the problem structure and goals. Gaussian Mixture Models (GMMs) describe complex data using multiple Gaussian distributions. Sampling methods help estimate GMM parameters via techniques like Expectation-Maximization or MCMC. Monte Carlo sampling explores probabilistic spaces effectively, while deterministic approaches ensure convergence. GMMs are valuable because they model heterogeneity, clustering, and uncertainty in a flexible probabilistic framework.Voraussetzung
- Voraussetzungen an Studierende
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- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Studiengangsbereiche
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- Ingenieurwissenschaften
Informatik
- Ingenieurwissenschaften
Betreuung
- Titel, Vorname, Name
- Ali Darijani
- Organisationseinheit
- Computer Science(IAR/IES)
- E-Mail Adresse
- ali.darijani@iosb.fraunhofer.de
- Link zur eigenen Homepage/Personenseite
- Website
Bewerbung per E-Mail
- Bewerbungsunterlagen
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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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