The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
We present quantitative models for the selection pressure of cellular evolutionary algorithms structured in two dimensional regular lattices. We derive models based on probabilisti...
Mario Giacobini, Enrique Alba, Andrea Tettamanzi, ...
Background: We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fa...
Pekka Marttinen, Adam Baldwin, William P. Hanage, ...
Cooperative multiagent probabilistic inference can be applied in areas such as building surveillance and complex system diagnosis to reason about the states of the distributed unc...