Abstract—This paper presents a stochastic modelling framework for complex biochemical reaction networks from a component-based perspective. Our approach takes into account the di...
Mila E. Majster-Cederbaum, Nils Semmelrock, Verena...
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy,...
The current state of the art in visualization research places a strong emphasis on different techniques to derive insight from disparate types of data. However, little work has in...
This paper integrates research in robot programming and reasoning about action with research in model-based reasoning about physical systems to provide a capability for modeling an...
We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, fo...