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Bayesian Learning of Continuous-Time Dynamical Systems

Lawrence — Sat, 27 Jun 2009 04:16:59 +0000

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.

dysii 1.4.0 released

Lawrence — Wed, 17 Dec 2008 15:53:03 +0000

Version 1.4.0 of the dysii Dynamic Systems Library has been released. This is a major new release with a number of additional features and performance enhancements, as well as representing a consolidation of code and maturation of much of the API.

Particular new features include:

The kernel forward-backward and two-filter smoothers, suitable for fast, large-scale approximate inference in continuous-time stochastic models, as documented in my recent PhD thesis.
Overhauled kd tree implementation, featuring distributed partitioning, dual-tree and self-tree evaluations, particularly useful for the new smoothers above.
Improved stochastic Runge-Kutta and new Euler-Maruyama method for integrating stochastic differential equations.
Performance improvements resulting from continued profiling, including more aggressive inlining and less dependence on virtuals.
A new installation guide, available in the INSTALL.txt file of the distribution. Also note that with Boost 1.35 now released, dysii no longer requires the latest CVS of Boost, making it much simpler to install.

Full details are included in the VERSION.txt file of the distribution.

A couple of examples of applications using dysii are expected to be released within a matter of days also. These should provide an excellent starting point for those wishing to use the library for their own work.

Thesis available

Lawrence — Mon, 15 Dec 2008 17:59:40 +0000

I’ve made my PhD thesis available on the research page, "Bayesian Learning of Continuous Time Dynamical Systems (with Applications in Functional Magnetic Resonance Imaging)". The thesis considers Bayesian filtering and smoothing for state and parameter estimation in general non-linear, non-Gaussian systems using stochastic differential models. It is the theoretical basis for impending updates to the dysii Dynamic Systems Library, including the kernel forward-backward and kernel two-filter smoothers, and distributed implementation of particle filters and kd trees.

I should note that this should be considered a draft version, as it is yet to be examined, and corrections may need to be made after it is. I’m providing it here mainly for the purposes of documenting dysii at this stage.

Continuous Time Particle Filtering for fMRI

Lawrence — Thu, 10 Jan 2008 17:11:50 +0000

The final version of the paper “Continuous Time Particle Filtering for fMRI”, presented as a poster at NIPS 2007, is now available.

Continuous Time Particle Filtering for fMRI

Lawrence — Thu, 29 Nov 2007 17:52:45 +0000

I’ve added a draft version of the paper Continuous Time Particle Filtering for fMRI to the research page, to be presented as a poster at NIPS 2007. Note that this version of the paper is not available in the conference pre-proceedings. The work makes use of both the dysii Dynamic Systems Library for distributed/parallel particle filtering and smoothing, and the fmrii fMRI Modelling Library, both available on this site.