A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
We introduce a generative probabilistic document model based on latent Dirichlet allocation (LDA), to deal with textual errors in the document collection. Our model is inspired by...
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Organ deformation between preoperative image data and the patient in the OR is the main obstacle for using surgical navigation systems in liver surgery. Our approach is to provide ...
Thomas Lange, Sebastian Eulenstein, Michael Hü...