We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
This paper combines a parameter generation algorithm and a model optimization approach with the model-integration-based voice conversion (MIVC). We have proposed probabilistic int...
Recently, the margin criterion has been successfully used for parameter optimization in graphical models. We introduce maximum margin based structure learning for Bayesian network...
In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/traini...
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynam...