In this paper, we describe a technique to construct accurate stochastic simulation models from acquired trace data. The resulting simulation models accept input trace data and ret...
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
Modeling and estimation of switching activities remain to be important problems in low-power design and fault analysis. A probabilistic Bayesian Network based switching model can ...
Large-scale simulations and computational modeling using molecular dynamics (MD) continues to make significant impacts in the field of biology. It is well known that simulations...
Sadaf R. Alam, Jeffrey S. Vetter, Pratul K. Agarwa...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...