Dense depth maps can be estimated in a Bayesian sense from multiple calibrated still images of a rigid scene relative to a reference view [1]. This well-established probabilistic f...
Peter Wey, Bernd Fischer, Herbert Bay, Joachim M. ...
Proper reuse of learning objects depends both on the amount and quality of attached semantic metadata such as “learning objective”', “related concept”, etc. Manually ...
Paramjeet Singh Saini, Marco Ronchetti, Diego Sona
This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing co...
Gregor Pavlin, Patrick de Oude, Marinus Maris, Jan...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Background: The empirical frequencies of DNA k-mers in whole genome sequences provide an interesting perspective on genomic complexity, and the availability of large segments of g...
Benny Chor, David Horn, Nick Goldman, Yaron Levy, ...