Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
—In this paper we consider the localization of a mobile station (MS) in time division multiple access (TDMA) based communication systems. We use joint angle and delay measurement...
—Vehicular networks face a typical quandary in their requirement for communications that are at once secure and private. While the messages broadcast between vehicles and between...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...