This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Background: Transposable elements (TE) are mobile genetic entities present in nearly all genomes. Previous work has shown that TEs tend to have a different nucleotide composition ...
We consider automated decision aids that help users select the best solution from a large set of options. For such tools to successfully accomplish their task, eliciting and repre...
: We study the satisfiability of randomly generated formulas formed by M clauses of exactly K literals over N Boolean variables. For a given value of N the problem is known to be m...
We examine a general Bayesian framework for constructing on-line prediction algorithms in the experts setting. These algorithms predict the bits of an unknown Boolean sequence usin...