Sequential inference for real-time probabilistic modelling

Project Details

Description

This project aims at advancing the state of the art in probabilistic modelling related to sensor fusion. This is accomplished by the combination of recent advances in probabilistic model specification methods from machine learning with powerful computational methods in signal processing and control engineering. The methodology allows noisy information streams to be combined into one model. This computational methodology will allow real-time probabilistic inference on mobile devices with limited resources.
StatusFinished
Effective start/end date01/09/201731/08/2020

Collaborative partners

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