Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property ...
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
We have developed models of how strategies are constructed and retained as male and female high school and university students gain experience in solving online qualitative chemic...
Ron Stevens, Amy Soller, Melanie Cooper, Marcia Sp...
We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Mar...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In ...