A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
This paper addresses the inference of the transcriptional regulatory network of Bacillus subtilis. Two inference approaches, a linear, additive model and a non-linear power-law mo...
Anshuman Gupta, Jeffrey D. Varner, Costas D. Maran...
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning. Recent ...
—This paper proposes the sequential context inference (SCI) algorithm for Markov random field (MRF) image analysis. This algorithm is designed primarily for fast inference on an...
Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The ...