Abstract- In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the depende...
Christian Spieth, Felix Streichert, Nora Speer, An...
This paper investigates new ways of inferring nonlinear dependence from measured data. The existence of unique linear and nonlinear sub-spaces which are structural invariants of g...
Douglas J. Leith, William E. Leithead, Roderick Mu...
Bayesian belief networks have grown to prominence because they provide compact representations of many domains, and there are algorithms to exploit this compactness. The next step...
Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with "rich get richer" dynamics. Inference for adaptor grammars seek...
Languages like ML and Haskell encourage the view of values as first-class entities that can be passed as arguments or results of functions, or stored as components of data struct...