On the Samplings of Gaussian Mixture
- Research topic/area
- Artificial Intelligence
- Type of thesis
- Bachelor / Master
- Start time
- -
- Application deadline
- 30.06.2028
- Duration of the thesis
- 4 months(BSc) - 6 months(MSc)
Description
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.Requirement
- Requirements for students
-
- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Faculty departments
-
- Engineering sciences
Informatics
- Engineering sciences
Supervision
- Title, first name, last name
- Ali Darijani
- Organizational unit
- Computer Science(IAR/IES)
- Email address
- ali.darijani@iosb.fraunhofer.de
- Link to personal homepage/personal page
- Website
Application via email
- Application documents
-
E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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