Now showing items 1-3 of 3

    • Bayesian generalised ensemble Markov chain Monte Carlo 

      Frellsen, Jes; Winther, Ole; Ghahramani, Zoubin; Ferkinghoff-Borg, Jesper (Microtome Publishing, 2016)
      Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in high-dimensional probability models: inapplicability to estimate ...
    • Beyond rotamers: a generative, probabilistic model of side chains in proteins 

      Harder, Tim; Boomsma, Wouter; Paluszewski, Martin; Frellsen, Jes; Johansson, Kristoffer E; Hamelryck, Thomas (2010-06-05)
      Abstract Background Accurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most ...
    • The Multivariate Generalised von Mises distribution: Inference and applications 

      Turner, Richard Eric; Frellsen, Jes; Navarro, Alexandre (The AAAI Press, 2017-02-13)
      Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this ...