Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each set missing vital information, which prevents accurate recovery of individual images. Although some recent (variational) methods have demonstrated high-resolution jointly recovering sequential images, there remain robustness...

The increasing size of data sets has lead to variable selection in regression becoming increasingly important. Bayesian approaches are attractive since they allow uncertainty about the choice of variables to be formally included in the analysis. The application of fully Bayesian variable selection methods to large data sets is computationally challenging. We describe an adaptive Markov chain Mo...

In a series of papers, we have formalized an active Bayesian perception approach for robotics based on recent progress in understanding animal perception. A central aspect of this active perception approach is the inference of ‘where’ and ‘what’ objects are in the environment under uncertainty, termed Simultaneous Object Localization and IDentification (SOLID). Here we describe some of the deta...