CORDIS Project
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This project aims to enhance approximate inference methods for probabilistic models used in machine learning and statistics. It focuses on developing techniques for Bayesian models and applying them to decision-making problems in robotics and bioinformatics.
We propose to develop and analyze approximate inference methods for probabilistic models.
Probabilistic models are widely used in Machine Learning to solve complex real-world problems and they also form an important research area in Statistics.
One of the biggest challenges in probabilistic modelling is to be able to infer marginal probabilities of some random variables in the model, a task which is often formally computationally intractable due to the complexity of the situation modeled.
We pro…
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