: As business transitions into the new economy, e-system successful use has become a strategic goal. Especially in business to consumer (e-commerce) applications, users highly eval...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
This paper considers the problem of knowledgebased model construction in the presence of uncertainty about the association of domain entities to random variables. Multi-entity Bay...
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...