We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...
Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes...
Geometric constraints are at the heart of parametric and feature-based CAD systems. Changing values of geometric constraint parameters is one of the most common operations in such...
Abstract. This paper addresses a parameter synthesis problem for nonlinear hybrid systems. Considering a set of uncertain parameters and a safety property, we give an algorithm tha...
Constraint-based mining has been proven to be extremely useful for supporting actionable pattern discovery. However, useful conjunctions of constraints that support domain driven ...