Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Background: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have...
Quantification of statistical significance is essential for the interpretation of protein structural similarity. To address this, a random model for protein structure comparison w...
In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora on the basis ...
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...