Compilation is an important approach to a range of inference problems, since it enables linear-time inference in the size S of the compiled representation. However, the main drawb...
Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated ...
A common problem in applied mathematics is that of finding a function in a Hilbert space with prescribed best approximations from a finite number of closed vector subspaces. In ...
An efficient method for rate control is illustrated for joint source-channel rate selection and for encoding under multiple leaky bucket buffer constraints. This method shows adva...
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...