The emerging science of simulation enables us to explore the dynamics of large and complex systems even if a formal representation and analysis of the system is intractable and a ...
The dynamically configurable distributed object (DCDO) model helps enable object evolution and facilitate the development of distributed objects from multiple independent implemen...
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...
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
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...