Projects / dysii


dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.


Recent releases

  •  17 Dec 2008 18:24

    Release Notes: This release adds kernel density estimators with distributed kd tree partitioning and dual-tree evaluations, an improved stochastic Runge-Kutta and new Euler-Maruyama integrator for stochastic differential equations, the kernel forward-backward and two-filter smoothers (from the author's PhD work), performance enhancements, and an installation guide.

    •  05 Mar 2008 18:10

      Release Notes: This version adds a stochastic Runge-Kutta method for stochastic differential equations, as well as density and kernel density (KD) trees for representing probability densities.

      •  02 Dec 2007 08:57

        Release Notes: An auxiliary particle filter was added and the resampling strategy framework was generalized. Diagonal covariance detection for optimized Gaussian density calculations was fixed. Several serialization bugs and a Wiener process variance bug were fixed.

        •  09 Oct 2007 21:48

          Release Notes: Overhauled parallel implementations. The particle smoother has been improved with further parallelisation. Distributed storage of mixtures has been added, as well as Gaussian mixture distributions and serialization of probability distributions. A Wiener process variance bug has been fixed.

          •  23 Aug 2007 20:54

            No changes have been submitted for this release.


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