A new spatial count data model with Bayesian additive regression trees for accident hot spot identification
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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 ...
The evaluation of avalanche release depths constitutes a great challenge for risk assessment in mountainous areas. This study focuses on slab avalanches, which generally result from the rupture of a weak layer underlying a cohesive slab. We use the finite- ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...
Model updating is useful for improving structural performance assessments. This paper examines an important assumption of traditional model-updating approaches. This assumption requires the error independence between points where predictions and measuremen ...
Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can gr ...
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 introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...
We show how variational Bayesian inference can be implemented for very large binary classification generalized linear models. Our relaxation is shown to be a convex problem for any log-concave model, and we provide an efficient double loop algorithm for so ...
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 ...
Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meetin ...
