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Large-eddy simulation (LES) of turbulence in plant canopies has traditionally been validated using bulk statistical quantities such as mean velocity and variance profiles. However, turbulent exchanges between a plant canopy and the atmosphere are dominated ...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probability. Its aim is showing in very informal terms how supervised learning can be interpreted from the Bayesian viewpoint. The focus is put on supervised learning ...
In this paper we consider the recovery of an airline schedule after an unforeseen event, commonly called disruption, that makes the planned schedule unfeasible. In particular we consider the aircraft recovery problem for an heterogeneous fleet of aircrafts ...
In this paper, we will present an efficient approach for distributed inference. We use belief propagation's message-passing algorithm on top of a DHT storing a Bayesian network. Nodes in the DHT run a variant of the spring relaxation algorithm to redistrib ...
In this paper we consider the recovery of an airline schedule after an unforeseen event, commonly called disruption, that makes the planned schedule unfeasible. In particular we consider the aircraft recovery problem for a quasi-homogeneous fleet of aircra ...
The environment that surrounds us is very complex. Understanding and interpreting it is a very hard task. This paper proposes an approach allowing simple form recognition with a camera by using a probabilistic approach called Bayesian Programming. The main ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...
Many facets of Bayesian Modelling are firmly established in Machine Learning and give rise to state-of-the-art solutions to application problems. The sheer number of techniques, ideas and models which have been proposed, and the terminology, can be bewilde ...
The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is mo ...
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate B ...