In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either ma...
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
In this paper, we present the Hidden Discrete Tempo Model, an effective Dynamic Bayesian Network for audio to score matching. Its main feature is an explicit modeling of tempo, wh...
We introduce a dynamical model for simultaneous registration and segmentation in a variational framework for image sequences, where the dynamics is incorporated using a Bayesian f...
Pratim Ghosh, Mehmet Emre Sargin, Bangalore S. Man...
The problem of dynamical tomography consists in reconstructing a temporal sequence of images from their noisy projections. For this purpose, a recursive algorithm is usually used,...