Note Dysii has been superseded by LibBi.

Dysii is a C++ library for distributed Bayesian inference in large-scale dynamical systems, scaling from single cores to hundreds of processors. It features the following:

Filtering and smoothing

  • Kalman filter and smoother
  • Rauch-Tung-Striebel (RTS) smoother
  • Unscented Kalman filter and smoother
  • Particle filter and forward-backward smoother
  • Kernel forward-backward and two-filter smoothers
  • Multiple resampling strategies for particle filters, including stratified, auxiliary and regularised resampling

Probability distributions

  • Gaussian and Gaussian mixture distributions
  • Dirac and Dirac mixture (weighted sample set) distributions
  • Kernel density estimators

Ordinary and stochastic differential equations

  • Adaptive numerical solvers for ordinary and stochastic differential equations, including Euler-Maruyama and stochastic Runge-Kutta
  • Autocorrelator and equilibrium distribution sampler

Parallelisation using MPI

  • Parallel particle filter and smoother
  • Parallel kernel forward-backward and two-filter smoother
  • Distributed storage of mixture densities
  • Distibuted kd tree evaluations, including dual- and self-tree evaluations

Data management

  • Serialization of vectors, matrices and probability distributions for fast and convenient data management, using Boost.Serialization
  • Text file reader and writer


  • Use of BLAS and LAPACK
  • Template meta-programming
  • Code profiling
  • Compiler optimisation

The library has been optimised for performance, while maintaining a modularity and generality that makes it suitable for a wide range of applications.

Related posts

LibBi released

Sunday, June 23rd, 2013

After four years of work, I’m very happy to announce that LibBi is now available as open source software. LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), [...]

New papers on arXiv.org

Wednesday, February 29th, 2012

I’ve added one preprint and one older workshop paper to arXiv.org, given recent interest, see below. Murray, L. M.; Jones, E. M. & Parslow, J. (2012). On collapsed state-space models and the particle marginal Metropolis-Hastings sampler. In review. [arXiv] Murray, L.M. (2011). GPU acceleration of the particle filter: The Metropolis resampler. Distributed machine learning and [...]

Bayesian Learning of Continuous-Time Dynamical Systems

Saturday, June 27th, 2009

I’ve posted the final version of my PhD thesis, "Bayesian Learning of Continuous-Time Dynamical Systems, with Applications in Functional Magnetic Resonance Imaging" to the research page. Now assessed, corrected and passed! Note that this may serve as a useful manual for some of the detail behind the algorithms of the dysii Dynamic Systems Library.

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