The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
Richard J. Boys, Darren J. Wilkinson, Thomas B. L....
We describe a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-toend data. We also investigate the problem of automat...
David Chiang, Jonathan Graehl, Kevin Knight, Adam ...
Initial studies have shown that automatic inference of high-level image quality or aesthetics is very challenging. The ability to do so, however, can prove beneficial in many appl...
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...