47 citations · 294 across the 31 of their papers we have counts for
12 papers · 1 filter
Variational State and Parameter Estimation
Jarrad Courts, Johannes Hendriks, Adrian Wills +2
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tract…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills +1
System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting natur…
Variational System Identification for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas Schön +1
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational infe…
Practical Bayesian System Identification using Hamiltonian Monte Carlo
Johannes Hendriks, Adrian Wills, Brett Ninness +1
This paper considers Bayesian parameter estimation of dynamic systems using a Markov Chain Monte Carlo (MCMC) approach. The Metroplis-Hastings (MH) algorithm is employed, and the m…
A Heteroscedastic Likelihood Model for Two-frame Optical Flow
Timothy Farnworth, Christopher Renton, Reuben Strydom +2
Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. V…
Bayesian Non-parametric Bragg-edge Fitting for Neutron Transmission Strain Imaging
Johannes Hendriks, Nicholas O'Dell, Adrian Wills +3
Energy resolved neutron transmission techniques can provide high-resolution images of strain within polycrystalline samples allowing the study of residual strain and stress in engi…
A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper presents a numerical…
A New Smoothing Algorithm for Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that p…
Bayesian Parameter Identification for Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertaint…
Gaussian Variational State Estimation for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas B. Schön
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the m…
Linearly Constrained Neural Networks
Johannes Hendriks, Carl Jidling, Adrian Wills +1
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints. To achiev…