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...
This paper deals with value (and Q-) function approximation in deterministic Markovian decision processes (MDPs). A general statistical framework based on the Kalman filtering pa...
In this paper, we present efficient HMM-based techniques for estimating missing features. By assuming speech features to be observations of hidden Markov processes, we derive a mi...
E cient and powerful topologically adaptable deformable surfaces can be created by embedding and de ning discrete deformable surface models in terms of an A ne Cell Decomposition (...
This work investigates how stochastic sampling jitter noise affects the result of system identification, and proposes a modification of known approaches to mitigate the effects of...