no code implementations • 14 Dec 2020 • Jarrad Courts, Johannes Hendriks, Adrian Wills, Thomas Schön, Brett Ninness
In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution.
no code implementations • 8 Dec 2020 • Jarrad Courts, Adrian Wills, Thomas Schön, Brett Ninness
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem.
1 code implementation • 26 Jun 2018 • Johan Dahlin, Adrian Wills, Brett Ninness
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for sampling from target distributions which are not easy to evaluate point-wise.
1 code implementation • 4 Jan 2018 • Johan Dahlin, Adrian Wills, Brett Ninness
The computation of Bayesian estimates of system parameters and functions of them on the basis of observed system performance data is a common problem within system identification.
Computation Computational Finance
no code implementations • 16 May 2017 • Adrian G. Wills, Johannes Hendriks, Christopher Renton, Brett Ninness
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures.